CS02 - Predicting Annual Air Pollution
Kean Sebastian (A17099624), Bella Hardy (A16369822), Shay Samat (A16816403)
Introduction
In our case study, we will explore the factors that can best predict air pollution in the United States, divided by regional zip codes. Air pollution causes more health problems for the overall public and the ecosystem of the United States than are accurately portrayed in the media. According to the State of Global Air [1], air pollution is the fifth leading cause of deaths in the entire world. Death by air pollution is more common than more commonly known risks such as malnutrition, alcohol, and overall physical health. Even more surprisingly, air pollution kills more people than malaria and road traffic accidents.
Though it may seem obvious that the United States is divided on how much their daily activities contribute to air pollution and, in turn, climate change. The Pew Research Center [2] found that 20% of Americans don’t think that they contribute to air pollution levels. Many academic journals state otherwise, such as the UCAR Center for Science [3], which says that air pollution is the direct cause of climate change, indicating that Americans don’t necessarily know there is a high chance that they contribute to air pollution every day. National Geographic [4] says that air pollution is all around us, in our car emissions, factories, aerosols, planes, smoking, wildfires, etc.
While the United States has shown overall improvements in air quality from previous years [5], as time goes on, more pollutants enter the atmosphere, meaning that air pollution is still a very serious threat to the well-being of the United States and globally. Without widespread air pollution monitoring, we continue to put ourselves at risk. When there are very minimal amounts of pollutant monitors in the US, much of the United States population could be suffering from extremely dangerous amounts of pollutants when some of the observations are overlapping their readings.
Our research question aims to address these issues: Can we predict US annual average air pollution concentrations at the granularity of zip code regional levels using a diverse set of predictors, including population density, urbanization, road density, satellite pollution data, and chemical modeling data? Predicting air pollution levels in our case study does more than just address a problem; it can actually be used to later influence county/zip code-wide policies on products and actions that cause pollution. And, of course, with better predictions, we can hopefully save lives in the long run.
Resources:
[1] Health Effects Institute. 2019. State of Global Air 2019, www.stateofglobalair.org
[2] Tyson, Alec. “Two-Thirds of Americans Think Government Should Do More on Climate.” Pew Research Center Science & Society, Pew Research Center, 23 June 2020, www.pewresearch.org/science/2020/06/23/two-thirds-of-americans-think-government-should-do-more-on-climate/#:~:text=Most%20U.S.%20adults%20think%20human,at%20all%20to%20climate%20change. Accessed 07 Dec. 2023.
[3] Education, UCAR Center for Science. “Center for Science Education.” Air Quality and Climate Change | Center for Science Education, scied.ucar.edu/learning-zone/air-quality/air-quality-and-climate-change#:~:text=Air%20pollution%20is%20causing%20the,can%20negatively%20impact%20air%20quality. Accessed 07 Dec. 2023. [4] “Air Pollution.” Education, education.nationalgeographic.org/resource/air-pollution/. Accessed 07 Dec. 2023.
[5] EPA, Environmental Protection Agency, gispub.epa.gov/air/trendsreport/2019/#home. Accessed 07 Dec. 2023.
Load packages
library(tidyverse)
library(dplyr)
library(ggplot2)
library(tidymodels)
library(yardstick)
library(parsnip)
library(pROC)
library(patchwork)
library(caret)
library(MLmetrics)pm <- readr::read_csv(here::here("OCS_data", "data","raw", "pm25_data.csv"))## Rows: 876 Columns: 50── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): state, county, city
## dbl (47): id, value, fips, lat, lon, CMAQ, zcta, zcta_area, zcta_pop, imp_a5...
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Question
Case Study 2 Prompt: Can we predict US annual average air pollution concentrations at the granularity of zip code regional levels using predictors such as data about population density, urbanization, road density, as well as, satellite pollution data and chemical modeling data?
This case study aims to explore the feasibility of predicting annual average air pollution concentrations in the United States at a granular level, specifically within zip code regional boundaries. By leveraging a diverse set of predictors including population density, urbanization metrics, road density, and integrating satellite pollution data along with chemical modeling information, the objective is to develop a comprehensive predictive model. This model could potentially offer insights into localized air quality variations, aiding in targeted interventions and policy-making efforts aimed at mitigating air pollution’s impact on public health and the environment.
Extension:
The Data
This dataset compiles diverse environmental and demographic information from monitoring stations across the United States. It includes air pollution estimates derived from a computational model (CMAQ), satellite-derived aerosol measurements (Aerosol Optical Depth), and emissions data from major sources within varying radii around monitoring sites. Additionally, it encompasses geographical details such as impervious surface measurements, road lengths, population densities, educational attainment percentages, poverty rates, and urban-rural classifications at the county and zip code levels. The dataset aims to offer a comprehensive view of potential predictors influencing air quality variations, allowing for granular analysis and prediction at localized levels like zip code areas, fostering insights valuable for targeted interventions and policy formulation to address air pollution concerns.
List of Features: id: Monitor number (county number before the decimal, monitor number after)
fips: Federal Information Processing Standard number for the county
Lat: Latitude of the monitor in degrees
Lon: Longitude of the monitor in degrees
state: State where the monitor is located
county: County where the monitor is located
city: City where the monitor is located
CMAQ: Estimated air pollution values from the Community Multiscale Air Quality model
zcta: Zip Code Tabulation Area where the monitor is located
zcta_area: Land area of the zip code area in square meters
zcta_pop: Population in the zip code area
imp_a500, imp_a1000, imp_a5000, imp_a10000, imp_a15000: Impervious surface measures within different radii around the monitor
county_area: Land area of the county of the monitor in square meters
county_pop: Population of the county of the monitor
Log_dist_to_prisec: Natural log distance to primary or secondary road from the monitor
log_pri_length_5000, log_pri_length_10000, log_pri_length_15000, log_pri_length_25000: Count of primary road length in meters within various radii around the monitor (Natural log)
log_prisec_length_500, log_prisec_length_1000, log_prisec_length_5000, log_prisec_length_10000, log_prisec_length_15000, log_prisec_length_25000: Count of primary and secondary road length in meters within various radii around the monitor (Natural log)
log_nei_2008_pm25_sum_10000, log_nei_2008_pm25_sum_15000, log_nei_2008_pm25_sum_25000: Tons of emissions from major sources within various radii around the monitor (Natural log)
log_nei_2008_pm10_sum_10000, log_nei_2008_pm10_sum_15000, log_nei_2008_pm10_sum_25000: Tons of emissions from major sources within various radii around the monitor (Natural log)
popdens_county: Population density of the county
popdens_zcta: Population density of the zip code area
nohs, somehs, hs, somecollege, associate, bachelor, grad: Educational attainment percentages in the zcta area
pov: Percentage of people in the zcta area living in poverty
hs_orless: Percentage of people in the zcta area with a high school degree or less
urc2013, urc2006: Urban-rural classification of the county
aod: Aerosol Optical Depth measurement from a NASA satellite, used as a proxy for particulate pollution
Data Import
pm <- read.csv("pm25_data.csv")
airports <- read.csv("airports2.csv")Data Wrangling
pm |>
glimpse()## Rows: 876
## Columns: 50
## $ id <dbl> 1003.001, 1027.000, 1033.100, 1049.100, 10…
## $ value <dbl> 9.597647, 10.800000, 11.212174, 11.659091,…
## $ fips <int> 1003, 1027, 1033, 1049, 1055, 1069, 1073, …
## $ lat <dbl> 30.49800, 33.28126, 34.75878, 34.28763, 33…
## $ lon <dbl> -87.88141, -85.80218, -87.65056, -85.96830…
## $ state <chr> "Alabama", "Alabama", "Alabama", "Alabama"…
## $ county <chr> "Baldwin", "Clay", "Colbert", "DeKalb", "E…
## $ city <chr> "Fairhope", "Ashland", "Muscle Shoals", "C…
## $ CMAQ <dbl> 8.098836, 9.766208, 9.402679, 8.534772, 9.…
## $ zcta <int> 36532, 36251, 35660, 35962, 35901, 36303, …
## $ zcta_area <dbl> 190980522, 374132430, 16716984, 203836235,…
## $ zcta_pop <int> 27829, 5103, 9042, 8300, 20045, 30217, 901…
## $ imp_a500 <dbl> 0.01730104, 1.96972318, 19.17301038, 5.782…
## $ imp_a1000 <dbl> 1.4096021, 0.8531574, 11.1448962, 3.867647…
## $ imp_a5000 <dbl> 3.3360118, 0.9851479, 15.1786154, 1.231141…
## $ imp_a10000 <dbl> 1.9879187, 0.5208189, 9.7253870, 1.0316469…
## $ imp_a15000 <dbl> 1.4386207, 0.3359198, 5.2472094, 0.9730444…
## $ county_area <dbl> 4117521611, 1564252280, 1534877333, 201266…
## $ county_pop <int> 182265, 13932, 54428, 71109, 104430, 10154…
## $ log_dist_to_prisec <dbl> 4.648181, 7.219907, 5.760131, 3.721489, 5.…
## $ log_pri_length_5000 <dbl> 8.517193, 8.517193, 8.517193, 8.517193, 9.…
## $ log_pri_length_10000 <dbl> 9.210340, 9.210340, 9.274303, 10.409411, 1…
## $ log_pri_length_15000 <dbl> 9.630228, 9.615805, 9.658899, 11.173626, 1…
## $ log_pri_length_25000 <dbl> 11.32735, 10.12663, 10.15769, 11.90959, 12…
## $ log_prisec_length_500 <dbl> 7.295356, 6.214608, 8.611945, 7.310155, 8.…
## $ log_prisec_length_1000 <dbl> 8.195119, 7.600902, 9.735569, 8.585843, 9.…
## $ log_prisec_length_5000 <dbl> 10.815042, 10.170878, 11.770407, 10.214200…
## $ log_prisec_length_10000 <dbl> 11.88680, 11.40554, 12.84066, 11.50894, 12…
## $ log_prisec_length_15000 <dbl> 12.205723, 12.042963, 13.282656, 12.353663…
## $ log_prisec_length_25000 <dbl> 13.41395, 12.79980, 13.79973, 13.55979, 13…
## $ log_nei_2008_pm25_sum_10000 <dbl> 0.318035438, 3.218632928, 6.573127301, 0.0…
## $ log_nei_2008_pm25_sum_15000 <dbl> 1.967358961, 3.218632928, 6.581917457, 3.2…
## $ log_nei_2008_pm25_sum_25000 <dbl> 5.067308, 3.218633, 6.875900, 4.887665, 4.…
## $ log_nei_2008_pm10_sum_10000 <dbl> 1.35588511, 3.31111648, 6.69187313, 0.0000…
## $ log_nei_2008_pm10_sum_15000 <dbl> 2.26783411, 3.31111648, 6.70127741, 3.3500…
## $ log_nei_2008_pm10_sum_25000 <dbl> 5.628728, 3.311116, 7.148858, 5.171920, 4.…
## $ popdens_county <dbl> 44.265706, 8.906492, 35.460814, 35.330814,…
## $ popdens_zcta <dbl> 145.716431, 13.639555, 540.887040, 40.7189…
## $ nohs <dbl> 3.3, 11.6, 7.3, 14.3, 4.3, 5.8, 7.1, 2.7, …
## $ somehs <dbl> 4.9, 19.1, 15.8, 16.7, 13.3, 11.6, 17.1, 6…
## $ hs <dbl> 25.1, 33.9, 30.6, 35.0, 27.8, 29.8, 37.2, …
## $ somecollege <dbl> 19.7, 18.8, 20.9, 14.9, 29.2, 21.4, 23.5, …
## $ associate <dbl> 8.2, 8.0, 7.6, 5.5, 10.1, 7.9, 7.3, 8.0, 4…
## $ bachelor <dbl> 25.3, 5.5, 12.7, 7.9, 10.0, 13.7, 5.9, 17.…
## $ grad <dbl> 13.5, 3.1, 5.1, 5.8, 5.4, 9.8, 2.0, 8.7, 2…
## $ pov <dbl> 6.1, 19.5, 19.0, 13.8, 8.8, 15.6, 25.5, 7.…
## $ hs_orless <dbl> 33.3, 64.6, 53.7, 66.0, 45.4, 47.2, 61.4, …
## $ urc2013 <int> 4, 6, 4, 6, 4, 4, 1, 1, 1, 1, 1, 1, 1, 2, …
## $ urc2006 <int> 5, 6, 4, 5, 4, 4, 1, 1, 1, 1, 1, 1, 1, 2, …
## $ aod <dbl> 37.36364, 34.81818, 36.00000, 33.08333, 43…skimr::skim(pm)| Name | pm |
| Number of rows | 876 |
| Number of columns | 50 |
| _______________________ | |
| Column type frequency: | |
| character | 3 |
| numeric | 47 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| state | 0 | 1 | 4 | 20 | 0 | 49 | 0 |
| county | 0 | 1 | 3 | 20 | 0 | 471 | 0 |
| city | 0 | 1 | 4 | 48 | 0 | 607 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id | 0 | 1 | 26987.96 | 1.578761e+04 | 1003.00 | 13089.15 | 26132.00 | 39118.00 | 5.603910e+04 | ▇▇▆▇▆ |
| value | 0 | 1 | 10.81 | 2.580000e+00 | 3.02 | 9.27 | 11.15 | 12.37 | 2.316000e+01 | ▂▆▇▁▁ |
| fips | 0 | 1 | 26987.89 | 1.578763e+04 | 1003.00 | 13089.00 | 26132.00 | 39118.00 | 5.603900e+04 | ▇▇▆▇▆ |
| lat | 0 | 1 | 38.48 | 4.620000e+00 | 25.47 | 35.03 | 39.30 | 41.66 | 4.840000e+01 | ▁▃▅▇▂ |
| lon | 0 | 1 | -91.74 | 1.496000e+01 | -124.18 | -99.16 | -87.47 | -80.69 | -6.804000e+01 | ▃▂▃▇▃ |
| CMAQ | 0 | 1 | 8.41 | 2.970000e+00 | 1.63 | 6.53 | 8.62 | 10.24 | 2.313000e+01 | ▃▇▃▁▁ |
| zcta | 0 | 1 | 50890.29 | 2.778447e+04 | 1022.00 | 28788.25 | 48172.00 | 74371.00 | 9.920200e+04 | ▅▇▇▅▇ |
| zcta_area | 0 | 1 | 183173481.91 | 5.425989e+08 | 15459.00 | 14204601.75 | 37653560.50 | 160041508.25 | 8.164821e+09 | ▇▁▁▁▁ |
| zcta_pop | 0 | 1 | 24227.58 | 1.777216e+04 | 0.00 | 9797.00 | 22014.00 | 35004.75 | 9.539700e+04 | ▇▇▃▁▁ |
| imp_a500 | 0 | 1 | 24.72 | 1.934000e+01 | 0.00 | 3.70 | 25.12 | 40.22 | 6.961000e+01 | ▇▅▆▃▂ |
| imp_a1000 | 0 | 1 | 24.26 | 1.802000e+01 | 0.00 | 5.32 | 24.53 | 38.59 | 6.750000e+01 | ▇▅▆▃▁ |
| imp_a5000 | 0 | 1 | 19.93 | 1.472000e+01 | 0.05 | 6.79 | 19.07 | 30.11 | 7.460000e+01 | ▇▆▃▁▁ |
| imp_a10000 | 0 | 1 | 15.82 | 1.381000e+01 | 0.09 | 4.54 | 12.36 | 24.17 | 7.209000e+01 | ▇▃▂▁▁ |
| imp_a15000 | 0 | 1 | 13.43 | 1.312000e+01 | 0.11 | 3.24 | 9.67 | 20.55 | 7.110000e+01 | ▇▃▁▁▁ |
| county_area | 0 | 1 | 3768701992.12 | 6.212830e+09 | 33703512.00 | 1116536297.50 | 1690826566.50 | 2878192209.00 | 5.194723e+10 | ▇▁▁▁▁ |
| county_pop | 0 | 1 | 687298.44 | 1.293489e+06 | 783.00 | 100948.00 | 280730.50 | 743159.00 | 9.818605e+06 | ▇▁▁▁▁ |
| log_dist_to_prisec | 0 | 1 | 6.19 | 1.410000e+00 | -1.46 | 5.43 | 6.36 | 7.15 | 1.045000e+01 | ▁▁▃▇▁ |
| log_pri_length_5000 | 0 | 1 | 9.82 | 1.080000e+00 | 8.52 | 8.52 | 10.05 | 10.73 | 1.205000e+01 | ▇▂▆▅▂ |
| log_pri_length_10000 | 0 | 1 | 10.92 | 1.130000e+00 | 9.21 | 9.80 | 11.17 | 11.83 | 1.302000e+01 | ▇▂▇▇▃ |
| log_pri_length_15000 | 0 | 1 | 11.50 | 1.150000e+00 | 9.62 | 10.87 | 11.72 | 12.40 | 1.359000e+01 | ▆▂▇▇▃ |
| log_pri_length_25000 | 0 | 1 | 12.24 | 1.100000e+00 | 10.13 | 11.69 | 12.46 | 13.05 | 1.436000e+01 | ▅▃▇▇▃ |
| log_prisec_length_500 | 0 | 1 | 6.99 | 9.500000e-01 | 6.21 | 6.21 | 6.21 | 7.82 | 9.400000e+00 | ▇▁▂▂▁ |
| log_prisec_length_1000 | 0 | 1 | 8.56 | 7.900000e-01 | 7.60 | 7.60 | 8.66 | 9.20 | 1.047000e+01 | ▇▅▆▃▁ |
| log_prisec_length_5000 | 0 | 1 | 11.28 | 7.800000e-01 | 8.52 | 10.91 | 11.42 | 11.83 | 1.278000e+01 | ▁▁▃▇▃ |
| log_prisec_length_10000 | 0 | 1 | 12.41 | 7.300000e-01 | 9.21 | 11.99 | 12.53 | 12.94 | 1.385000e+01 | ▁▁▃▇▅ |
| log_prisec_length_15000 | 0 | 1 | 13.03 | 7.200000e-01 | 9.62 | 12.59 | 13.13 | 13.57 | 1.441000e+01 | ▁▁▃▇▅ |
| log_prisec_length_25000 | 0 | 1 | 13.82 | 7.000000e-01 | 10.13 | 13.38 | 13.92 | 14.35 | 1.523000e+01 | ▁▁▃▇▆ |
| log_nei_2008_pm25_sum_10000 | 0 | 1 | 3.97 | 2.350000e+00 | 0.00 | 2.15 | 4.29 | 5.69 | 9.120000e+00 | ▆▅▇▆▂ |
| log_nei_2008_pm25_sum_15000 | 0 | 1 | 4.72 | 2.250000e+00 | 0.00 | 3.47 | 5.00 | 6.35 | 9.420000e+00 | ▃▃▇▇▂ |
| log_nei_2008_pm25_sum_25000 | 0 | 1 | 5.67 | 2.110000e+00 | 0.00 | 4.66 | 5.91 | 7.28 | 9.650000e+00 | ▂▂▇▇▃ |
| log_nei_2008_pm10_sum_10000 | 0 | 1 | 4.35 | 2.320000e+00 | 0.00 | 2.69 | 4.62 | 6.07 | 9.340000e+00 | ▅▅▇▇▂ |
| log_nei_2008_pm10_sum_15000 | 0 | 1 | 5.10 | 2.180000e+00 | 0.00 | 3.87 | 5.39 | 6.72 | 9.710000e+00 | ▂▃▇▇▂ |
| log_nei_2008_pm10_sum_25000 | 0 | 1 | 6.07 | 2.010000e+00 | 0.00 | 5.10 | 6.37 | 7.52 | 9.880000e+00 | ▁▂▆▇▃ |
| popdens_county | 0 | 1 | 551.76 | 1.711510e+03 | 0.26 | 40.77 | 156.67 | 510.81 | 2.682191e+04 | ▇▁▁▁▁ |
| popdens_zcta | 0 | 1 | 1279.66 | 2.757490e+03 | 0.00 | 101.15 | 610.35 | 1382.52 | 3.041884e+04 | ▇▁▁▁▁ |
| nohs | 0 | 1 | 6.99 | 7.210000e+00 | 0.00 | 2.70 | 5.10 | 8.80 | 1.000000e+02 | ▇▁▁▁▁ |
| somehs | 0 | 1 | 10.17 | 6.200000e+00 | 0.00 | 5.90 | 9.40 | 13.90 | 7.220000e+01 | ▇▂▁▁▁ |
| hs | 0 | 1 | 30.32 | 1.140000e+01 | 0.00 | 23.80 | 30.75 | 36.10 | 1.000000e+02 | ▂▇▂▁▁ |
| somecollege | 0 | 1 | 21.58 | 8.600000e+00 | 0.00 | 17.50 | 21.30 | 24.70 | 1.000000e+02 | ▆▇▁▁▁ |
| associate | 0 | 1 | 7.13 | 4.010000e+00 | 0.00 | 4.90 | 7.10 | 8.80 | 7.140000e+01 | ▇▁▁▁▁ |
| bachelor | 0 | 1 | 14.90 | 9.710000e+00 | 0.00 | 8.80 | 12.95 | 19.22 | 1.000000e+02 | ▇▂▁▁▁ |
| grad | 0 | 1 | 8.91 | 8.650000e+00 | 0.00 | 3.90 | 6.70 | 11.00 | 1.000000e+02 | ▇▁▁▁▁ |
| pov | 0 | 1 | 14.95 | 1.133000e+01 | 0.00 | 6.50 | 12.10 | 21.22 | 6.590000e+01 | ▇▅▂▁▁ |
| hs_orless | 0 | 1 | 47.48 | 1.675000e+01 | 0.00 | 37.92 | 48.65 | 59.10 | 1.000000e+02 | ▁▃▇▃▁ |
| urc2013 | 0 | 1 | 2.92 | 1.520000e+00 | 1.00 | 2.00 | 3.00 | 4.00 | 6.000000e+00 | ▇▅▃▂▁ |
| urc2006 | 0 | 1 | 2.97 | 1.520000e+00 | 1.00 | 2.00 | 3.00 | 4.00 | 6.000000e+00 | ▇▅▃▂▁ |
| aod | 0 | 1 | 43.70 | 1.956000e+01 | 5.00 | 31.66 | 40.17 | 49.67 | 1.430000e+02 | ▃▇▁▁▁ |
pm <- na.omit(pm)names(pm)## [1] "id" "value"
## [3] "fips" "lat"
## [5] "lon" "state"
## [7] "county" "city"
## [9] "CMAQ" "zcta"
## [11] "zcta_area" "zcta_pop"
## [13] "imp_a500" "imp_a1000"
## [15] "imp_a5000" "imp_a10000"
## [17] "imp_a15000" "county_area"
## [19] "county_pop" "log_dist_to_prisec"
## [21] "log_pri_length_5000" "log_pri_length_10000"
## [23] "log_pri_length_15000" "log_pri_length_25000"
## [25] "log_prisec_length_500" "log_prisec_length_1000"
## [27] "log_prisec_length_5000" "log_prisec_length_10000"
## [29] "log_prisec_length_15000" "log_prisec_length_25000"
## [31] "log_nei_2008_pm25_sum_10000" "log_nei_2008_pm25_sum_15000"
## [33] "log_nei_2008_pm25_sum_25000" "log_nei_2008_pm10_sum_10000"
## [35] "log_nei_2008_pm10_sum_15000" "log_nei_2008_pm10_sum_25000"
## [37] "popdens_county" "popdens_zcta"
## [39] "nohs" "somehs"
## [41] "hs" "somecollege"
## [43] "associate" "bachelor"
## [45] "grad" "pov"
## [47] "hs_orless" "urc2013"
## [49] "urc2006" "aod"pm <- pm |>
rename(
ImpSrf_500m = imp_a500,
ImpSrf_1000m = imp_a1000,
ImpSrf_5000m = imp_a5000,
ImpSrf_10000m = imp_a10000,
ImpSrf_15000m = imp_a15000,
CountyArea_m2 = county_area,
CountyPop = county_pop,
Log_Dist_PrscRd = log_dist_to_prisec,
Log_PriRdLen_5000m = log_pri_length_5000,
Log_PriRdLen_10000m = log_pri_length_10000,
Log_PriRdLen_15000m = log_pri_length_15000,
Log_PriRdLen_25000m = log_pri_length_25000,
Log_PrscRdLen_500m = log_prisec_length_500,
Log_PrscRdLen_1000m = log_prisec_length_1000,
Log_PrscRdLen_5000m = log_prisec_length_5000,
Log_PrscRdLen_10000m = log_prisec_length_10000,
Log_PrscRdLen_15000m = log_prisec_length_15000,
Log_PrscRdLen_25000m = log_prisec_length_25000,
Log_NEI_PM25_Sum_10000m = log_nei_2008_pm25_sum_10000,
Log_NEI_PM25_Sum_15000m = log_nei_2008_pm25_sum_15000,
Log_NEI_PM25_Sum_25000m = log_nei_2008_pm25_sum_25000,
Log_NEI_PM10_Sum_10000m = log_nei_2008_pm10_sum_10000,
Log_NEI_PM10_Sum_15000m = log_nei_2008_pm10_sum_15000,
Log_NEI_PM10_Sum_25000m = log_nei_2008_pm10_sum_25000
)
names(pm)## [1] "id" "value"
## [3] "fips" "lat"
## [5] "lon" "state"
## [7] "county" "city"
## [9] "CMAQ" "zcta"
## [11] "zcta_area" "zcta_pop"
## [13] "ImpSrf_500m" "ImpSrf_1000m"
## [15] "ImpSrf_5000m" "ImpSrf_10000m"
## [17] "ImpSrf_15000m" "CountyArea_m2"
## [19] "CountyPop" "Log_Dist_PrscRd"
## [21] "Log_PriRdLen_5000m" "Log_PriRdLen_10000m"
## [23] "Log_PriRdLen_15000m" "Log_PriRdLen_25000m"
## [25] "Log_PrscRdLen_500m" "Log_PrscRdLen_1000m"
## [27] "Log_PrscRdLen_5000m" "Log_PrscRdLen_10000m"
## [29] "Log_PrscRdLen_15000m" "Log_PrscRdLen_25000m"
## [31] "Log_NEI_PM25_Sum_10000m" "Log_NEI_PM25_Sum_15000m"
## [33] "Log_NEI_PM25_Sum_25000m" "Log_NEI_PM10_Sum_10000m"
## [35] "Log_NEI_PM10_Sum_15000m" "Log_NEI_PM10_Sum_25000m"
## [37] "popdens_county" "popdens_zcta"
## [39] "nohs" "somehs"
## [41] "hs" "somecollege"
## [43] "associate" "bachelor"
## [45] "grad" "pov"
## [47] "hs_orless" "urc2013"
## [49] "urc2006" "aod"Analysis
Exploratory Data Analysis
There are 49 elements in the state column. This is because only the 48 states within the continental U.S. are included, as well as the District of Columbia.
pm %>%
distinct(state) ## state
## 1 Alabama
## 2 Arizona
## 3 Arkansas
## 4 California
## 5 Colorado
## 6 Connecticut
## 7 Delaware
## 8 District Of Columbia
## 9 Florida
## 10 Georgia
## 11 Idaho
## 12 Illinois
## 13 Indiana
## 14 Iowa
## 15 Kansas
## 16 Kentucky
## 17 Louisiana
## 18 Maine
## 19 Maryland
## 20 Massachusetts
## 21 Michigan
## 22 Minnesota
## 23 Mississippi
## 24 Missouri
## 25 Montana
## 26 Nebraska
## 27 Nevada
## 28 New Hampshire
## 29 New Jersey
## 30 New Mexico
## 31 New York
## 32 North Carolina
## 33 North Dakota
## 34 Ohio
## 35 Oklahoma
## 36 Oregon
## 37 Pennsylvania
## 38 Rhode Island
## 39 South Carolina
## 40 South Dakota
## 41 Tennessee
## 42 Texas
## 43 Utah
## 44 Vermont
## 45 Virginia
## 46 Washington
## 47 West Virginia
## 48 Wisconsin
## 49 WyomingInterestingly, when comparing monitors between San Diego County and Baltimore County, there are fewer monitors in San Diego. This is despite San Diego having a much larger county population and a much larger county area. This could be correlated to county population density, as the population density for Baltimore is significantly higher than that of San Diego.
pm %>% filter(city == "San Diego")## id value fips lat lon state county city
## 1 6073.001 11.30495 6073 32.83646 -117.1288 California San Diego San Diego
## 2 6073.101 13.74281 6073 32.70149 -117.1497 California San Diego San Diego
## CMAQ zcta zcta_area zcta_pop ImpSrf_500m ImpSrf_1000m ImpSrf_5000m
## 1 9.679734 92123 21148247 26823 56.96540 42.59862 29.75533
## 2 9.679734 92113 13647793 56066 62.73443 57.59321 38.41929
## ImpSrf_10000m ImpSrf_15000m CountyArea_m2 CountyPop Log_Dist_PrscRd
## 1 29.90047 31.03464 10895120648 3095313 5.889929
## 2 33.50957 33.20612 10895120648 3095313 6.439666
## Log_PriRdLen_5000m Log_PriRdLen_10000m Log_PriRdLen_15000m
## 1 11.77990 12.84633 13.49200
## 2 11.57337 12.79770 13.25116
## Log_PriRdLen_25000m Log_PrscRdLen_500m Log_PrscRdLen_1000m
## 1 13.88062 7.925588 9.681314
## 2 13.79533 6.214608 8.828726
## Log_PrscRdLen_5000m Log_PrscRdLen_10000m Log_PrscRdLen_15000m
## 1 11.86692 12.93122 13.59976
## 2 11.71745 12.94720 13.45715
## Log_PrscRdLen_25000m Log_NEI_PM25_Sum_10000m Log_NEI_PM25_Sum_15000m
## 1 14.19471 6.701290 6.788967
## 2 14.00775 5.154103 6.171575
## Log_NEI_PM25_Sum_25000m Log_NEI_PM10_Sum_10000m Log_NEI_PM10_Sum_15000m
## 1 6.902245 6.853830 6.949341
## 2 7.151224 5.371998 6.340358
## Log_NEI_PM10_Sum_25000m popdens_county popdens_zcta nohs somehs hs
## 1 7.076172 284.1008 1268.332 2.30 4.60 22.60
## 2 7.313310 284.1008 4108.063 10.25 9.65 21.95
## somecollege associate bachelor grad pov hs_orless urc2013 urc2006 aod
## 1 28.6 9.00 21.80 11.100 6.800 29.50 1 1 53.0
## 2 23.6 10.25 16.35 7.975 12.125 41.85 1 1 74.2pm %>% filter(city == "Baltimore")## id value fips lat lon state county city
## 1 24510 12.20417 24510 39.34056 -76.58222 Maryland Baltimore (City) Baltimore
## 2 24510 12.53158 24510 39.34465 -76.68538 Maryland Baltimore (City) Baltimore
## 3 24510 12.79009 24510 39.28777 -76.54686 Maryland Baltimore (City) Baltimore
## 4 24510 14.28625 24510 39.23278 -76.57972 Maryland Baltimore (City) Baltimore
## 5 24510 13.31776 24510 39.29773 -76.60460 Maryland Baltimore (City) Baltimore
## CMAQ zcta zcta_area zcta_pop ImpSrf_500m ImpSrf_1000m ImpSrf_5000m
## 1 10.94099 21251 461424 934 23.86332 24.40061 30.29091
## 2 10.94099 21215 17645223 60161 39.58910 36.68274 20.15881
## 3 10.94099 21224 24539976 49134 52.56747 42.72426 40.15900
## 4 10.94099 21226 25718732 7561 47.92993 52.78222 27.00408
## 5 10.94099 21202 4111039 22832 54.16609 60.98106 47.61408
## ImpSrf_10000m ImpSrf_15000m CountyArea_m2 CountyPop Log_Dist_PrscRd
## 1 30.97307 24.00093 209643241 620961 6.290041
## 2 24.94368 20.92504 209643241 620961 4.896128
## 3 31.35098 24.34897 209643241 620961 6.029092
## 4 30.85959 24.50620 209643241 620961 6.053460
## 5 31.35133 26.10478 209643241 620961 6.327766
## Log_PriRdLen_5000m Log_PriRdLen_10000m Log_PriRdLen_15000m
## 1 10.30081 12.46166 13.13467
## 2 10.61036 12.08638 13.06048
## 3 11.40118 12.52707 13.06208
## 4 11.40602 12.63250 13.28804
## 5 11.25765 12.49259 13.32613
## Log_PriRdLen_25000m Log_PrscRdLen_500m Log_PrscRdLen_1000m
## 1 13.68658 6.214608 9.451706
## 2 13.71012 7.788901 9.099535
## 3 13.69727 7.212027 9.387663
## 4 13.80005 6.990040 8.780156
## 5 13.74749 6.214608 9.087255
## Log_PrscRdLen_5000m Log_PrscRdLen_10000m Log_PrscRdLen_15000m
## 1 12.08663 13.60081 14.25590
## 2 11.87033 13.44102 14.10281
## 3 12.21872 13.62511 14.19648
## 4 12.12845 13.60566 14.29857
## 5 12.42890 13.60451 14.36452
## Log_PrscRdLen_25000m Log_NEI_PM25_Sum_10000m Log_NEI_PM25_Sum_15000m
## 1 14.87934 5.054359 5.830902
## 2 14.94211 3.118629 5.103638
## 3 14.85238 7.213761 8.282681
## 4 14.93222 8.285675 8.291580
## 5 14.91886 5.093924 8.287900
## Log_NEI_PM25_Sum_25000m Log_NEI_PM10_Sum_10000m Log_NEI_PM10_Sum_15000m
## 1 8.325895 5.672080 6.365952
## 2 8.318862 3.189317 5.720812
## 3 8.326663 7.682122 8.596778
## 4 8.324531 8.594286 8.603195
## 5 8.325156 5.699463 8.595958
## Log_NEI_PM10_Sum_25000m popdens_county popdens_zcta nohs somehs hs
## 1 8.639569 2961.989 2024.1687 0.0 0.0 0.0
## 2 8.626094 2961.989 3409.4780 6.6 16.6 32.8
## 3 8.640220 2961.989 2002.2024 13.1 14.5 26.6
## 4 8.637902 2961.989 293.9881 9.2 13.9 37.1
## 5 8.638950 2961.989 5553.8271 6.2 18.6 27.1
## somecollege associate bachelor grad pov hs_orless urc2013 urc2006 aod
## 1 0.0 71.4 0.0 28.6 3.525 0.0 1 1 53.50000
## 2 21.0 4.6 11.1 7.3 21.200 56.0 1 1 53.41667
## 3 15.4 3.6 15.6 11.1 15.900 54.2 1 1 78.60000
## 4 16.9 4.9 12.5 5.5 7.200 60.2 2 2 78.60000
## 5 15.5 3.1 15.1 14.3 28.800 51.9 1 1 73.81818This plot measures the correlation between each variable in the dataset. This is important to because we want to know which variables are actually predictive. If there are variables that show little to no correlation, we should not need to consider them when we generate our model as they are unnecessary. In the following plot, dark blue equates to a strong positive correlation why dark red equates to a strong negative correlation. Relationships that are not very strong are represented with lighter shades of these colors.
We can see that there is very strong correlation between the variables related to impervious surface measure, including the totals. This seems to show that if the area within a 500 meter radius of a monitor has a lot of impervious surfaces, we can expect the area within up to a 15000 meter radius of the same monitor to have a lot of impervious surfaces. We can see the same kind of correlation with the lengths of roads (primary and primary + secondary) within various radii of the monitors. However, the totals don’t seem to have as strong of a correlation. There is also a strong correlation between the total emissions for coarse and fine particulate matter. If a monitor reports a high amount of emissions for coarse particulate matter, we can expect a high amount of emissions for fine particulate matter as well. This correlation applies for all measured radii around each monitor. We noticed negative correlations between measures for impervious surfaces and road lengths when compared with URC scores from both 2006 and 2013. This makes sense, as a higher URC scale indicates a rural environment, which would have less roads and impervious surfaces.
PM_cor <- cor(pm %>% dplyr::select_if(is.numeric))
corrplot::corrplot(PM_cor, tl.cex = 0.5)
With this plot, we are visualizing the strengths of correlations without regard to positivity or negativity.
corrplot::corrplot(abs(PM_cor), order = "hclust", tl.cex = 0.5, cl.lim = c(0, 1))## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt =
## tl.srt, : "cl.lim" is not a graphical parameter## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col =
## tl.col, : "cl.lim" is not a graphical parameter## Warning in title(title, ...): "cl.lim" is not a graphical parameter
This group of scatterplots visualizes the correlations between impervious surface measures within the various radii around monitors from 500 meters to 15000 meters. The correlation between the 500 meters and 15000 meters is the weakest, at 0.594, although this number still indicates a positive correlation. The strongest correlation is between 500 meters and 1000 meters, at 0.973. Thus, we can see that impervious surfaces are strongly correlated when comparing any radius.
# we used GGally in a previous set of notes
# will need to install if you haven't yet
select(pm, contains("imp")) %>%
GGally::ggpairs()## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
This group of scatterplots visualizes the correlations between total emissions of both coarse and fine particulate matter within the various radii around the monitors from 10000 meters to 25000 meters. The correlation between fine particulate matter at 25000 meters and coarse particulate matter at 10000 meters is the weakest, at 0.717, which is still quite strong, and positive. The strongest correlation is between fine particulate matter at 15000 meters and coarse particulate matter at 15000 meters, at 0.990. Thus, we can see that total emissions of both coarse and fine particulate matter are strongly correlated when comparing any radius, and have a much stronger overall correlation when compared to impervious surface measure.
select(pm, contains("nei")) %>%
GGally::ggpairs()
This correlation plot visualizes the correlations between primary road lengths, primary and secondary road lengths, and the distances between the monitors to primary and secondary roads. We can see that the strongest positive correlations are between primary road length at 10000 meters and 15000 meters, and between primary and secondary road length at the same radii. The distance from monitors to primary and secondary roads is strongly negatively correlated with primary and secondary road lengths at radii of 500 and 1000 meters. Across the plot, we can see positive correlations between road length measures and negative correlations when we compare these to distances.
select(pm, contains("Rd")) %>%
GGally::ggcorr(hjust = .85, size = 3,
layout.exp=2, label = TRUE)
This group of scatterplots visualizes the correlations between total emissions of fine particulate matter within a radius of 10000 meters, county populations, population densities, primary road lengths within a radius of 10000 meters, and impervious surface measure within 10000 meters. The correlation between fine particulate matter and county population is the weakest, at 0.176, which indicates very little relationship. This means that raw population number at the county level is not necessarily predictive of total emissions in that ares. The strongest correlation is between primary road length and impervious surface measure, at 0.649, which makes sense since roads are impervious surfaces themselves. and if there are more roads, there are likely more buildings in the area along those roads.
pm %>%
select(Log_NEI_PM25_Sum_10000m, popdens_county,
Log_PriRdLen_10000m, ImpSrf_10000m, CountyPop) %>%
GGally::ggpairs()
This group of scatterplots visualizes the correlations between the same variables as above, except that county populations and population densities have been converted to a logarithmic scale. Taking into account these changes, we can see that the relationships involving these changed variables have stronger correlations. Now the strongest correlation is between county population and population density, at 0.790. The weakest correlation is still between fine particulate matter and county population, but the strength has increased, at 0.377.
pm %>%
mutate(log_popdens_county=log(popdens_county),
log_pop_county = log(CountyPop)) %>%
select(Log_NEI_PM25_Sum_10000m, log_popdens_county,
Log_PriRdLen_10000m, ImpSrf_10000m, log_pop_county) %>%
GGally::ggpairs()
This set of visualizations plots the tons of pollution within a 10000 meter radius of the monitors in comparison to the amount of impervious surfaces within the same area. The graphs are color coded by the urban-rural classification of each monitor. The second graph is a facet wrap where the points are separated by URC value. Based on this visual, there seems to be little relation between the tons of pollution and the amount of impervious surfaces; high pollution exists in areas with few impervious surfaces as well as areas with many impervious surfaces. However, we can see that the urban-rural classification has a relation to the amount of impervious surfaces.
ggplot(pm, aes(x=ImpSrf_10000m, y=Log_NEI_PM25_Sum_10000m, color=urc2013)) +
geom_jitter()
ggplot(pm, aes(x=ImpSrf_10000m, y=Log_NEI_PM25_Sum_10000m, color=urc2013)) +
geom_jitter() +
facet_wrap(~urc2013)
This set of visualizations plots the relationships between estimated CMAQ values and the percentage of the population with Bachelor’s degrees, the percentage of the population with at most high school degrees, population densities, and the amount of roads within a 10000 meter radius of the monitors. The first two graphs compare the estimated CMAQ values in areas with different education levels. They reveal that most monitors are in areas where the percentage of people with Bachelor’s degrees is less than 50%, while the distribution of percentages of people with at most high school degrees is much wider. In both of these graphs, we can see that monitors in urban areas generally have higher CMAQ values than those in rural areas.
ggplot(pm, aes(x=bachelor, y=CMAQ, color=urc2013)) +
geom_jitter()
ggplot(pm, aes(x=hs_orless, y=CMAQ, color=urc2013)) +
geom_jitter()
This graph compares CMAQ values to the amount of roads within a 10000 meter radius of the monitors. Here we see rural areas have less roads compared to urban areas, and the amount of pollution seems to vary more in areas with less roads than areas with more roads.
ggplot(pm, aes(x=Log_PrscRdLen_1000m, y=CMAQ, color=urc2013)) +
geom_jitter()
This set of visualizations plots each of these points by latitude and longitude, which reveals the relative locations of each monitor. The first graph simply shows where urban areas are and where rural areas are.
ggplot(pm, aes(x=lon, y=lat, color=urc2013)) +
geom_jitter()
This graph shows which areas have more tons of pollution within a 10000 meter radius of the monitors. Here, compared with the graph above, it seems most pollution is in more urban areas.
ggplot(pm, aes(x=lon, y=lat, color=Log_NEI_PM25_Sum_10000m)) +
geom_jitter() +
scale_color_continuous(high = "#132B43", low = "#b9e4ff")
This graph demonstrates the air pollution concentrations per state, it is clear that some states such as California, New York, Texas, and Louisiana had the highest range of air pollution concentrations. We were interested if population density played a role in this concentration.
ggplot(pm, aes(x = state, y = CMAQ)) +
geom_boxplot() +
labs(x = "State",
y = "Air Pollution Concentrations (CMAQ)",
title = "Distribution of Air Pollution Concentrations by State") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
The graph above demonstrates the population density between the states, and it is clear that highest population density correlates a decent amount to air pollution concentration, however there’s also some drastic differences as well such as due to the size of texas being so big, the air pollution concentration is high, but the population density is low, but new york is high for both variables.
ggplot(pm, aes(x = state, y = popdens_zcta)) +
geom_bar(stat = "summary", fun = "mean", fill = "skyblue") +
labs(x = "State", y = "Average Population Density",
title = "Average Population Density by State") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
These boxplots above show the variability and central tendencies of air pollution estimates (‘CMAQ’) across different classifications of urban-rural areas in 2013 and 2006. They highlight potential differences or similarities in pollution levels based on these urban-rural classifications. We can see similar CMAQ scores for each classification between 2006 and 2013, although the 75th percentile has noticeably increased for score 6 in 2013. And while the median was slightly lower for score 5 compared to score 6, they are now at nearly the same level.
boxplot_urc2013 <- ggplot(pm, aes(x = factor(urc2013), y = CMAQ)) +
geom_boxplot(fill = "lightcoral") +
labs(title = "Boxplot of CMAQ by URC 2013", x = "URC 2013", y = "CMAQ")
boxplot_urc2006 <- ggplot(pm, aes(x = factor(urc2006), y = CMAQ)) +
geom_boxplot(fill = "lightyellow") +
labs(title = "Boxplot of CMAQ by URC 2006", x = "URC 2006", y = "CMAQ")
boxplot_urc2013
boxplot_urc2006
Data Analysis
Here we are converting columns for id, fips and zcta into factors since these represent categorical variables. We also create a new variable that indicates whether a monitor is in a city or not in a city, which will make it easier to categorize each monitor. Then we split 2/3 of our data to be used for training the random forest model we will be generating later. The remaining 1/3 will be used for testing our model.
# Converting to factors as discussed last class
pm <- pm %>%
dplyr::mutate(across(c(id, fips, zcta), as.factor))
pm <- pm %>%
mutate(city = case_when(city == "Not in a city" ~ "Not in a city",
city != "Not in a city" ~ "In a city"))
set.seed(1234) # same seed as before
pm_split <-rsample::initial_split(data = pm, prop = 2/3)
train_pm <-rsample::training(pm_split)
test_pm <-rsample::testing(pm_split)Here we write our recipe for pre-processing our training data by specifying which variables should be considered as predictors, and which should be considered the outcome. We also want to make sure that we specify the variables that should not considered as predictors, since they are simply used for identification purposes. These include the id and fips variables, which are identification for each monitor and each county, respectively. We also remove variables that are highly correlated and those with non-zero variance as these will impact the accuracy of our model.
RF_rec <- recipe(train_pm) %>%
update_role(everything(), new_role = "predictor")%>%
update_role(value, new_role = "outcome")%>%
update_role(id, new_role = "id variable") %>%
update_role("fips", new_role = "county id") %>%
step_novel("state") %>%
step_string2factor("state", "county", "city") %>%
step_rm("county") %>%
step_rm("zcta") %>%
step_corr(all_numeric())%>%
step_nzv(all_numeric())Here we are setting the parameters for our model. We set the number of predictor variables that will be randomly sampled to 10 and the number of data points required for a node to be split further to 3. We set the modeling engine to randomForest and the mode of the model to regression, since we want our model to be a Random Forest that predicts our outcome variable.
# install.packages("randomForest")
RF_PM_model <- parsnip::rand_forest(mtry = 10, min_n = 3) %>%
set_engine("randomForest") %>%
set_mode("regression")
RF_PM_model## Random Forest Model Specification (regression)
##
## Main Arguments:
## mtry = 10
## min_n = 3
##
## Computational engine: randomForestNow we create a workflow that will combine our pre-processing recipe with the model we created. Then, we fit this workflow to our training dataset.
RF_wflow <- workflows::workflow() %>%
workflows::add_recipe(RF_rec) %>%
workflows::add_model(RF_PM_model)
RF_wflow## ══ Workflow ════════════════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: rand_forest()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 6 Recipe Steps
##
## • step_novel()
## • step_string2factor()
## • step_rm()
## • step_rm()
## • step_corr()
## • step_nzv()
##
## ── Model ───────────────────────────────────────────────────────────────────────
## Random Forest Model Specification (regression)
##
## Main Arguments:
## mtry = 10
## min_n = 3
##
## Computational engine: randomForestRF_wflow_fit <- parsnip::fit(RF_wflow, data = train_pm)
RF_wflow_fit## ══ Workflow [trained] ══════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: rand_forest()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 6 Recipe Steps
##
## • step_novel()
## • step_string2factor()
## • step_rm()
## • step_rm()
## • step_corr()
## • step_nzv()
##
## ── Model ───────────────────────────────────────────────────────────────────────
##
## Call:
## randomForest(x = maybe_data_frame(x), y = y, mtry = min_cols(~10, x), nodesize = min_rows(~3, x))
## Type of random forest: regression
## Number of trees: 500
## No. of variables tried at each split: 10
##
## Mean of squared residuals: 2.633327
## % Var explained: 59.29This code allows us to see the top 10 most important features in making predictions. We can see that state is the most important feature by a large margin compared to the second most important feature, which is CMAQ. Total emissions for coarse particulate matter within radii from 10000 to 25000 meters are also important predictors to keep in mind.
RF_wflow_fit %>%
extract_fit_parsnip() %>%
vip::vip(num_features = 10)
Here we perform a v-fold cross-validation with 4 folds using our training data. Then we tune our Random Forest workflow by training the model on subsets of our training data as defined by the cross-validation folds. Afterward, we collect the performance metrics from the tuned model. Our R-squared is 0.6, which indicates pretty good fit as it is closer to 1 than it is to 0.
set.seed(1234)
vfold_pm <- rsample::vfold_cv(data = train_pm, v = 4)set.seed(456)
resample_RF_fit <- tune::fit_resamples(RF_wflow, vfold_pm)
collect_metrics(resample_RF_fit)## # A tibble: 2 × 6
## .metric .estimator mean n std_err .config
## <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 rmse standard 1.67 4 0.101 Preprocessor1_Model1
## 2 rsq standard 0.591 4 0.0514 Preprocessor1_Model1We will create a tuned model which will decide the number of predictor variables that will be randomly sampled and the number of data points required for a node to be split further for us. We set our model engine to randomForest and the mode to regression like before.
tune_RF_model <- rand_forest(mtry = tune(), min_n = tune()) %>%
set_engine("randomForest") %>%
set_mode("regression")
tune_RF_model## Random Forest Model Specification (regression)
##
## Main Arguments:
## mtry = tune()
## min_n = tune()
##
## Computational engine: randomForestWe create a new workflow that uses our tuned model.
RF_tune_wflow <- workflows::workflow() %>%
workflows::add_recipe(RF_rec) %>%
workflows::add_model(tune_RF_model)
RF_tune_wflow## ══ Workflow ════════════════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: rand_forest()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 6 Recipe Steps
##
## • step_novel()
## • step_string2factor()
## • step_rm()
## • step_rm()
## • step_corr()
## • step_nzv()
##
## ── Model ───────────────────────────────────────────────────────────────────────
## Random Forest Model Specification (regression)
##
## Main Arguments:
## mtry = tune()
## min_n = tune()
##
## Computational engine: randomForestWe detect how many CPU cores we have access to.
n_cores <- parallel::detectCores()
n_cores## [1] 32We can do parallel processing with that number of cores. We perform hyperparameter tuning on our new workflow and perform resampling with our previously created v-fold cross validation object. A grid search will be performed with 20 different combinations of hyperparameters. The goal here is to find which combination of hyperparameters will optimize our model’s performance.
# install.packages("doParallel")
doParallel::registerDoParallel(cores = n_cores)
set.seed(123)
tune_RF_results <- tune_grid(object = RF_tune_wflow, resamples = vfold_pm, grid = 20)## i Creating pre-processing data to finalize unknown parameter: mtryLooking at the results of our hyperparameter tuning, the best number of predictor variables to be randomly sampled is 32 and the best number of data points to be required for a node to be split further is 11.
tune_RF_results## Warning: This tuning result has notes. Example notes on model fitting include:
## preprocessor 1/1, model 15/20: 36 columns were requested but there were 35 predictors in the data. 35 will be used.## # Tuning results
## # 4-fold cross-validation
## # A tibble: 4 × 4
## splits id .metrics .notes
## <list> <chr> <list> <list>
## 1 <split [438/146]> Fold1 <tibble [40 × 6]> <tibble [0 × 1]>
## 2 <split [438/146]> Fold2 <tibble [40 × 6]> <tibble [0 × 1]>
## 3 <split [438/146]> Fold3 <tibble [40 × 6]> <tibble [1 × 1]>
## 4 <split [438/146]> Fold4 <tibble [40 × 6]> <tibble [0 × 1]>tune_RF_results %>%
collect_metrics()## # A tibble: 40 × 8
## mtry min_n .metric .estimator mean n std_err .config
## <int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 12 33 rmse standard 1.72 4 0.0866 Preprocessor1_Model01
## 2 12 33 rsq standard 0.562 4 0.0466 Preprocessor1_Model01
## 3 27 35 rmse standard 1.69 4 0.102 Preprocessor1_Model02
## 4 27 35 rsq standard 0.563 4 0.0511 Preprocessor1_Model02
## 5 22 40 rmse standard 1.71 4 0.106 Preprocessor1_Model03
## 6 22 40 rsq standard 0.556 4 0.0543 Preprocessor1_Model03
## 7 1 27 rmse standard 2.03 4 0.0501 Preprocessor1_Model04
## 8 1 27 rsq standard 0.440 4 0.0245 Preprocessor1_Model04
## 9 6 32 rmse standard 1.77 4 0.0756 Preprocessor1_Model05
## 10 6 32 rsq standard 0.552 4 0.0435 Preprocessor1_Model05
## # ℹ 30 more rowsshow_best(tune_RF_results, metric = "rmse", n = 1)## # A tibble: 1 × 8
## mtry min_n .metric .estimator mean n std_err .config
## <int> <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 32 11 rmse standard 1.65 4 0.113 Preprocessor1_Model10Now that we know the best combination of parameters to use, we can finalize our workflow to use the tuned hyperparameter values. We then fit our finalized Random Forest model to both the training dataset and the testing dataset. We can see similar results for root mean squared and R squared between this finalized model and our previous results.
tuned_RF_values<- select_best(tune_RF_results, "rmse")
tuned_RF_values## # A tibble: 1 × 3
## mtry min_n .config
## <int> <int> <chr>
## 1 32 11 Preprocessor1_Model10# specify best combination from tune in workflow
RF_tuned_wflow <-RF_tune_wflow %>%
tune::finalize_workflow(tuned_RF_values)
# fit model with those parameters on train AND test
overallfit <- RF_wflow %>%
tune::last_fit(pm_split)
collect_metrics(overallfit)## # A tibble: 2 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 rmse standard 1.72 Preprocessor1_Model1
## 2 rsq standard 0.610 Preprocessor1_Model1We can see that our model predicts the values fairly accurately as the differences between most of the predicted values and actual values are quite small.
test_predictions <- collect_predictions(overallfit)
test_predictions## # A tibble: 292 × 5
## id .pred .row value .config
## <chr> <dbl> <int> <dbl> <chr>
## 1 train/test split 12.0 3 11.2 Preprocessor1_Model1
## 2 train/test split 11.6 5 12.4 Preprocessor1_Model1
## 3 train/test split 11.0 6 10.5 Preprocessor1_Model1
## 4 train/test split 13.3 7 15.6 Preprocessor1_Model1
## 5 train/test split 12.0 8 12.4 Preprocessor1_Model1
## 6 train/test split 10.6 9 11.1 Preprocessor1_Model1
## 7 train/test split 11.1 14 11.8 Preprocessor1_Model1
## 8 train/test split 11.1 16 10.0 Preprocessor1_Model1
## 9 train/test split 11.6 18 12.0 Preprocessor1_Model1
## 10 train/test split 12.0 20 13.2 Preprocessor1_Model1
## # ℹ 282 more rowspm$row_indices <- seq_len(nrow(pm))
test_predictions$row_indices <- test_predictions$.row
# Merge the datasets using the row indices
merged_data <- merge(pm, test_predictions, by = "row_indices", all.x = TRUE)
cleaned_data <- merged_data[!is.na(merged_data$.row), ]
cleaned_data## row_indices id.x value.x fips lat lon
## 3 3 1033.1002 11.212174 1033 34.75878 -87.65056
## 5 5 1055.001 12.375394 1055 33.99375 -85.99107
## 6 6 1069.0003 10.508850 1069 31.22636 -85.39077
## 7 7 1073.0023 15.591017 1073 33.55306 -86.81500
## 8 8 1073.1005 12.399355 1073 33.33111 -87.00361
## 9 9 1073.1009 11.103704 1073 33.45972 -87.30556
## 14 14 1073.5003 11.775000 1073 33.80167 -86.94250
## 16 16 1097.0003 10.032110 1097 30.76994 -88.08753
## 18 18 1101.0007 11.997867 1101 32.42583 -86.28528
## 20 20 1113.0001 13.163146 1113 32.47220 -85.00510
## 24 24 1127.0002 11.705833 1127 33.83278 -87.27250
## 26 26 4003.1005 6.911111 4003 31.34920 -109.53968
## 27 27 4005.1008 5.928814 4005 35.20611 -111.65278
## 28 28 4013.0019 10.492617 4013 33.48385 -112.14257
## 33 33 4015.1 5.528205 4015 35.54002 -113.41078
## 37 37 4021.3002 7.465179 4021 33.42119 -111.50322
## 42 42 5001.0011 11.249704 5001 34.51852 -91.55896
## 44 44 5035.0005 11.714754 5035 35.19729 -90.19314
## 46 46 5051.0003 11.153097 5051 34.46944 -93.00018
## 47 47 5067.0001 11.186885 5067 35.63729 -91.18891
## 48 48 5107.0001 11.058974 5107 34.52977 -90.58605
## 50 50 5115.0003 11.259322 5115 35.25977 -93.10005
## 52 52 5119.1004 12.119167 5119 34.72960 -92.24359
## 56 56 5143.0005 10.685088 5143 36.17970 -94.11683
## 62 62 6009.0001 10.164062 6009 38.20185 -120.68157
## 65 65 6019.0008 18.677009 6019 36.78133 -119.77319
## 68 68 6023.1002 7.600000 6023 40.80178 -124.16210
## 69 69 6023.1004 8.217241 6023 40.77694 -124.17750
## 72 72 6025.1003 8.082407 6025 32.79222 -115.56306
## 73 73 6027.1003 6.695946 6027 36.48782 -117.87104
## 78 78 6029.0016 23.160784 6029 35.32293 -118.99917
## 80 80 6033.3001 9.030159 6033 39.03270 -122.92229
## 81 81 6037.0002 14.213396 6037 34.13650 -117.92391
## 82 82 6037.1002 13.929661 6037 34.17605 -118.31712
## 89 89 6037.4004 13.709169 6037 33.79236 -118.17533
## 90 90 6037.9033 7.120000 6037 34.67139 -118.13146
## 92 92 6047.251 16.694872 6047 37.30854 -120.48099
## 93 93 6053.1003 7.166197 6053 36.69676 -121.63718
## 94 94 6057.0005 14.300000 6057 39.23433 -121.05659
## 96 96 6059.0007 12.765789 6059 33.83062 -117.93845
## 99 99 6063.1006 15.281356 6063 39.93710 -120.93882
## 109 109 6069.0002 6.936667 6069 36.84410 -121.36212
## 110 110 6071.0025 15.815044 6071 34.03722 -117.69000
## 111 111 6071.0306 8.644860 6071 34.51001 -117.33143
## 114 114 6071.9004 13.346847 6071 34.10688 -117.27411
## 115 115 6073.0001 12.305128 6073 32.63123 -117.05908
## 117 117 6073.0006 11.304951 6073 32.83646 -117.12875
## 119 119 6073.101 13.742812 6073 32.70149 -117.14965
## 129 129 6085.0005 12.265587 6085 37.34850 -121.89490
## 130 130 6087.0007 6.767213 6087 36.98392 -121.98933
## 134 134 6097.0003 9.078495 6097 38.44350 -122.71017
## 137 137 6107.2002 20.648092 6107 36.33218 -119.29123
## 139 139 6111.0009 9.693220 6111 34.40427 -118.80995
## 140 140 6111.2002 10.683193 6111 34.27636 -118.68376
## 144 144 8005.0005 7.184746 8005 39.60440 -105.01953
## 148 148 8031.0023 8.218750 8031 39.78108 -104.95665
## 151 151 8039.0001 4.318333 8039 39.23138 -104.63477
## 153 153 8069.0009 6.679167 8069 40.57129 -105.07969
## 154 154 8077.0017 9.112712 8077 39.06380 -108.56117
## 158 158 9001.001 11.921101 9001 41.17083 -73.19472
## 167 167 9009.0027 11.286893 9009 41.30140 -72.90287
## 172 172 10001.0002 11.368696 10001 38.98667 -75.55680
## 173 173 10001.0003 11.276786 10001 39.15500 -75.51806
## 175 175 10003.1007 11.484259 10003 39.55130 -75.73200
## 177 177 10003.2004 13.529223 10003 39.73944 -75.55806
## 180 180 11001.0042 12.436975 11001 38.87626 -77.03406
## 185 185 12009.0007 7.555372 12009 28.05361 -80.62861
## 193 193 12057.003 8.552956 12057 27.93194 -82.50972
## 196 196 12073.0012 10.567376 12073 30.43972 -84.34639
## 197 197 12086.0033 7.967826 12086 25.94194 -80.32639
## 200 200 12095.1004 7.545810 12095 28.55083 -81.34556
## 202 202 12099.0008 6.196026 12099 26.72444 -80.66667
## 209 209 12115.0013 6.931373 12115 27.29056 -82.50722
## 213 213 13051.0017 11.916340 13051 32.09296 -81.14399
## 217 217 13067.0003 13.743363 13067 34.01548 -84.60741
## 219 219 13089.0002 12.713120 13089 33.68797 -84.29048
## 220 220 13089.2001 13.073278 13089 33.90139 -84.27990
## 230 230 13215.0001 13.492857 13215 32.48354 -84.98098
## 232 232 13215.0011 13.163303 13215 32.42905 -84.93160
## 233 233 13223.0003 11.893966 13223 33.92850 -85.04534
## 236 236 13295.0002 12.475000 13295 34.97889 -85.30098
## 239 239 16001.001 7.912658 16001 43.60070 -116.34785
## 240 240 16001.0011 7.718627 16001 43.63611 -116.27028
## 242 242 16009.001 9.050442 16009 47.31667 -116.57028
## 244 244 16059.0004 9.720690 16059 45.18190 -113.89029
## 245 245 16079.0017 11.701630 16079 47.53639 -116.23667
## 246 246 17001.0007 9.181356 17001 39.91541 -91.33587
## 247 247 17019.0004 10.487931 17019 40.12380 -88.22953
## 253 253 17031.0076 11.905882 17031 41.75140 -87.71349
## 257 257 17031.3301 12.045378 17031 41.78277 -87.80538
## 261 261 17043.4002 11.291525 17043 41.77107 -88.15253
## 264 264 17089.0003 10.850000 17089 42.05040 -88.28002
## 266 266 17097.1007 9.360656 17097 42.46757 -87.81005
## 268 268 17111.0001 10.090090 17111 42.22144 -88.24221
## 273 273 17119.2009 12.410909 17119 38.90308 -90.14317
## 282 282 17197.1011 10.361667 17197 41.22154 -88.19097
## 283 283 17201.0013 10.709167 17201 42.26308 -89.09277
## 287 287 18037.0004 12.198000 18037 38.36944 -86.95903
## 288 288 18037.0005 12.535455 18037 38.40478 -86.92832
## 290 290 18039.0008 12.255856 18039 41.65691 -85.96837
## 300 300 18089.0031 12.227097 18089 41.59851 -87.34299
## 301 301 18089.2004 12.875653 18089 41.58550 -87.47449
## 304 304 18095.0009 12.184770 18095 40.11198 -85.67995
## 311 311 18141.0014 11.327500 18141 41.66346 -86.20783
## 312 312 18141.0015 12.376243 18141 41.69669 -86.21468
## 314 314 18157.0008 11.598977 18157 40.43164 -86.85250
## 315 315 18163.0006 12.266463 18163 37.97173 -87.56724
## 316 316 18163.0012 12.533333 18163 38.02173 -87.56946
## 317 317 18163.0016 12.453846 18163 37.97444 -87.53229
## 320 320 19013.0008 10.389916 19013 42.49306 -92.34389
## 322 322 19045.0021 10.997085 19045 41.87500 -90.17757
## 323 323 19055.0001 10.244444 19055 42.60083 -91.53849
## 325 325 19111.0008 11.435593 19111 40.40096 -91.39101
## 333 333 19155.0009 10.277391 19155 41.26417 -95.89612
## 336 336 19163.0019 13.464000 19163 41.51777 -90.61875
## 337 337 19177.0006 9.269643 19177 40.69508 -92.00632
## 339 339 19197.0004 9.154369 19197 42.69539 -93.65598
## 342 342 20107.0002 9.754545 20107 38.13588 -94.73199
## 351 351 21019.0017 12.074380 21019 38.45934 -82.64041
## 355 355 21047.0006 11.982051 21047 36.91171 -87.32334
## 356 356 21059.0005 12.016522 21059 37.78078 -87.07531
## 359 359 21073.0006 11.491597 21073 38.21936 -84.83850
## 360 360 21093.0006 12.231818 21093 37.70561 -85.85263
## 366 366 21117.0007 12.015702 21117 39.07250 -84.52500
## 368 368 21151.0003 10.459664 21151 37.73846 -84.28495
## 369 369 21183.0032 11.991667 21183 37.31972 -86.95610
## 370 370 21195.0002 10.552941 21195 37.48260 -82.53532
## 373 373 22019.0009 8.764078 22019 30.22778 -93.57833
## 374 374 22019.001 9.251887 22019 30.17714 -93.21451
## 376 376 22033.1001 9.660000 22033 30.59398 -91.25194
## 377 377 22047.0005 11.648113 22047 30.21910 -91.06255
## 385 385 22087.0007 10.584314 22087 29.94475 -89.97626
## 386 386 22105.0001 10.312587 22105 30.50306 -90.37694
## 391 391 24005.1007 11.709756 24005 39.46203 -76.63167
## 392 392 24005.3001 12.553754 24005 39.31083 -76.47444
## 393 393 24015.0003 13.223598 24015 39.70144 -75.86005
## 396 396 24033.0025 12.508772 24033 38.94121 -76.93219
## 401 401 24510.0007 12.531579 24510 39.34465 -76.68538
## 406 406 25005.1004 9.045902 25005 41.68571 -71.16924
## 407 407 25009.2006 8.713158 25009 42.47464 -70.97082
## 411 411 25013.0016 10.768908 25013 42.10899 -72.59080
## 412 412 25013.2009 10.809917 25013 42.10579 -72.59713
## 415 415 25025.0002 11.157018 25025 42.34887 -71.09716
## 416 416 25025.0027 10.668966 25025 42.37212 -71.06174
## 422 422 26017.0014 8.935088 26017 43.57139 -83.89072
## 423 423 26021.0014 9.898230 26021 42.19779 -86.30969
## 425 425 26033.0902 10.572500 26033 46.48167 -84.33167
## 435 435 26113.0001 6.522807 26113 44.31056 -84.89186
## 436 436 26115.0005 11.400855 26115 41.76389 -83.47194
## 437 437 26121.004 9.594253 26121 43.23306 -86.23858
## 438 438 26125.0001 10.895763 26125 42.46306 -83.18320
## 443 443 26163.0015 12.864754 26163 42.30279 -83.10653
## 446 446 26163.0025 10.958120 26163 42.42306 -83.42626
## 447 447 26163.0033 13.398319 26163 42.30667 -83.14875
## 451 451 27021.0001 4.744828 27021 47.15994 -94.15099
## 452 452 27037.047 9.490598 27037 44.73846 -93.23725
## 453 453 27053.0961 9.447500 27053 44.87551 -93.25892
## 458 458 27109.5008 10.176364 27109 43.99691 -92.45037
## 464 464 27137.7551 7.807563 27137 46.76643 -92.13354
## 467 467 27163.0446 8.405000 27163 45.02798 -92.77415
## 472 472 28043.0001 10.332174 28043 33.83444 -89.79278
## 473 473 28047.0008 9.804310 28047 30.39037 -89.04978
## 477 477 28075.0003 12.421849 28075 32.36456 -88.73149
## 483 483 29047.0005 10.109565 29047 39.30309 -94.37662
## 490 490 29510.0085 12.939266 29510 38.65650 -90.19865
## 492 492 29510.0093 13.363559 29510 38.65648 -90.18987
## 493 493 30013.1026 5.367213 30013 47.50222 -111.27889
## 494 494 30029.0007 8.893407 30029 48.38111 -114.17472
## 496 496 30029.0047 7.913115 30029 48.20054 -114.30533
## 503 503 30063.0021 7.850000 30063 47.17710 -113.48270
## 506 506 30089.0007 7.191964 30089 47.59439 -115.32375
## 507 507 30093.0005 10.190164 30093 46.00260 -112.50125
## 509 509 31055.0019 9.181564 31055 41.24749 -95.97314
## 514 514 31157.0003 6.783810 31157 41.86500 -103.66444
## 515 515 31177.0002 8.215534 31177 41.55121 -96.14617
## 516 516 32003.0561 9.284706 32003 36.16396 -115.11392
## 517 517 32003.1019 4.521622 32003 35.78567 -115.35705
## 524 524 33013.1006 8.712637 33013 43.13244 -71.45831
## 525 525 33015.0014 8.195082 33015 43.07533 -70.74800
## 527 527 34001.0006 10.230928 34001 39.46487 -74.44874
## 528 528 34001.1006 10.771875 34001 39.36326 -74.43100
## 532 532 34003.0009 12.334146 34003 40.83311 -74.04346
## 533 533 34007.0003 12.749593 34007 39.92304 -75.09762
## 535 535 34013.0015 12.957143 34013 40.73029 -74.21274
## 536 536 34015.0004 11.622936 34015 39.83081 -75.28472
## 549 549 34041.0006 11.166964 34041 40.69921 -75.18053
## 550 550 35001.0023 5.978932 35001 35.13430 -106.58520
## 553 553 35013.0017 12.436905 35013 31.79583 -106.55750
## 555 555 35017.1002 5.072381 35017 32.78444 -108.27167
## 556 556 35025.0008 6.211215 35025 32.72666 -103.12292
## 558 558 35045.0006 5.879747 35045 36.72750 -108.22083
## 559 559 35049.002 4.509322 35049 35.67111 -105.95361
## 566 566 36029.0005 10.692285 36029 42.87691 -78.80953
## 568 568 36031.0003 4.298276 36031 44.39308 -73.85890
## 575 575 36067.1015 8.150476 36067 43.05235 -76.05921
## 579 579 36085.0067 10.692308 36085 40.59664 -74.12525
## 581 581 36101.0003 7.913750 36101 42.09142 -77.20978
## 583 583 36119.1002 10.957143 36119 40.93149 -73.76575
## 589 589 37051.0009 12.348538 37051 35.04142 -78.95311
## 603 603 37117.0001 11.136134 37117 35.81066 -76.90625
## 604 604 37119.0041 12.395157 37119 35.24010 -80.78568
## 610 610 37147.0005 11.761538 37147 35.59417 -77.38611
## 611 611 37147.0006 11.853846 37147 35.63861 -77.35805
## 614 614 37173.0002 11.199145 37173 35.43477 -83.44213
## 616 616 37183.002 11.393043 37183 35.72880 -78.68020
## 617 617 37189.0003 9.153659 37189 36.22194 -81.66306
## 619 619 38007.0002 4.574138 38007 46.89430 -103.37853
## 621 621 38017.1004 8.327826 38017 46.93375 -96.85535
## 622 622 38057.0004 6.516129 38057 47.29861 -101.76694
## 625 625 39017.0016 13.789831 39017 39.33839 -84.56660
## 626 626 39023.0005 12.796581 39023 39.92882 -83.80949
## 628 628 39035.0027 13.222642 39035 41.48089 -81.70380
## 630 630 39035.0038 14.039241 39035 41.47701 -81.68238
## 631 631 39035.0045 13.638182 39035 41.47178 -81.65679
## 633 633 39035.0065 14.566102 39035 41.44668 -81.66242
## 636 636 39049.0025 12.441667 39049 39.92845 -82.98104
## 640 640 39061.0014 15.162147 39061 39.19433 -84.47897
## 650 650 39093.3002 11.372674 39093 41.46307 -82.11426
## 652 652 39095.0025 12.129268 39095 41.66579 -83.47611
## 653 653 39095.0026 12.345045 39095 41.62057 -83.64226
## 655 655 39099.0014 13.135294 39099 41.09594 -80.65852
## 657 657 39113.0032 13.359103 39113 39.76066 -84.18768
## 662 662 39151.002 12.450746 39151 40.80073 -81.37301
## 664 664 39153.0023 12.967544 39153 41.08796 -81.54161
## 669 669 40101.0169 11.555652 40101 35.75527 -95.37767
## 672 672 40115.9004 9.997414 40115 36.92222 -94.83889
## 674 674 40135.9015 11.084946 40135 35.58117 -94.83006
## 677 677 41017.012 5.221277 41017 44.06392 -121.31255
## 680 680 41029.1001 6.915517 41029 42.53611 -122.87500
## 684 684 41039.0058 8.422034 41039 44.06630 -123.13983
## 686 686 41039.1009 7.311290 41039 44.04670 -123.01770
## 689 689 41051.008 8.355085 41051 45.49664 -122.60288
## 690 690 41051.0246 7.737190 41051 45.56137 -122.66790
## 691 691 41059.0121 8.283193 41059 45.65223 -118.82303
## 692 692 41061.0119 6.715254 41061 45.33900 -118.09521
## 693 693 41067.0004 9.746154 41067 45.52850 -122.97240
## 694 694 42001.0001 11.427353 42001 39.92002 -77.30968
## 695 695 42003.0008 12.981311 42003 40.46542 -79.96076
## 698 698 42003.1008 13.518692 42003 40.61749 -79.72766
## 702 702 42011.0011 12.412931 42011 40.38335 -75.96860
## 703 703 42017.0012 12.533981 42017 40.10722 -74.88222
## 704 704 42021.0011 13.896721 42021 40.30972 -78.91500
## 705 705 42027.01 10.821448 42027 40.81139 -77.87703
## 709 709 42045.0002 13.870513 42045 39.83556 -75.37250
## 710 710 42049.0003 10.724633 42049 42.14175 -80.03861
## 711 711 42069.2006 10.077364 42069 41.44278 -75.62306
## 714 714 42091.0013 11.761468 42091 40.11222 -75.30917
## 715 715 42095.0025 12.267778 42095 40.62806 -75.34111
## 718 718 42101.0047 13.346515 42101 39.94465 -75.16521
## 720 720 42101.0057 13.094075 42101 39.96006 -75.14222
## 722 722 42125.02 12.380909 42125 40.17056 -80.26139
## 723 723 42125.5001 11.020625 42125 40.44528 -80.42083
## 725 725 42133.0008 13.402367 42133 39.96528 -76.69944
## 728 728 44007.0026 10.678632 44007 41.87467 -71.37997
## 730 730 44007.101 9.027193 44007 41.84157 -71.36077
## 734 734 45037.0001 12.163063 45037 33.73996 -81.85363
## 735 735 45041.0002 11.410909 45041 34.16764 -79.85040
## 741 741 45079.0007 12.383193 45079 34.09396 -80.96230
## 749 749 46071.0001 5.252941 46071 43.74561 -101.94122
## 752 752 46103.002 7.740496 46103 44.08740 -103.27378
## 753 753 46103.1001 6.715126 46103 44.07835 -103.22824
## 756 756 47065.4002 12.578903 47065 35.05092 -85.29302
## 759 759 48037.0004 11.167213 48037 33.42576 -94.07080
## 762 762 48113.005 11.487719 48113 32.77426 -96.79769
## 765 765 48135.0003 9.272131 48135 31.83657 -102.34204
## 768 768 48141.0044 11.859016 48141 31.76569 -106.45523
## 769 769 48141.0053 16.151786 48141 31.75853 -106.50105
## 770 770 48201.0024 11.776271 48201 29.90104 -95.32614
## 771 771 48201.0058 10.778947 48201 29.77070 -95.03123
## 785 785 49005.0004 9.704315 49005 41.73111 -111.83750
## 797 797 49057.1003 8.654310 49057 41.30361 -111.98787
## 800 800 50007.0012 7.759574 50007 44.48028 -73.21444
## 805 805 51041.0003 11.301639 51041 37.43467 -77.45118
## 807 807 51059.1005 11.229167 51059 38.83738 -77.16338
## 809 809 51069.001 12.063333 51069 39.28102 -78.08157
## 814 814 51165.0003 11.498198 51165 38.47753 -78.81952
## 815 815 51520.0006 10.589167 51520 36.60800 -82.16410
## 818 818 51710.0024 13.664641 51710 36.85555 -76.30135
## 820 820 51770.0015 11.701420 51770 37.29717 -79.95573
## 830 830 54003.0003 14.158261 54003 39.44801 -77.96412
## 837 837 54039.1005 14.201709 54039 38.36618 -81.69372
## 838 838 54049.0006 13.716379 54049 39.48148 -80.13467
## 843 843 54107.1002 13.856637 54107 39.32353 -81.55237
## 845 845 55009.0005 11.751882 55009 44.50729 -87.99344
## 851 851 55063.0012 11.835537 55063 43.77750 -91.22690
## 855 855 55079.0043 12.694958 55079 43.02641 -87.91111
## 856 856 55079.0059 13.318333 55079 42.95500 -87.93417
## 857 857 55079.0099 13.058333 55079 43.03987 -87.92079
## 859 859 55089.0009 11.272500 55089 43.49806 -87.81000
## 864 864 55133.0027 13.546400 55133 43.02007 -88.21507
## 870 870 56021.0001 4.500000 56021 41.13998 -104.81780
## 874 874 56035.0705 6.173494 56035 42.87053 -109.86096
## state county city CMAQ zcta
## 3 Alabama Colbert In a city 9.402679 35660
## 5 Alabama Etowah In a city 9.241744 35901
## 6 Alabama Houston In a city 9.121892 36303
## 7 Alabama Jefferson In a city 10.235612 35207
## 8 Alabama Jefferson Not in a city 10.235612 35111
## 9 Alabama Jefferson Not in a city 8.161789 35444
## 14 Alabama Jefferson Not in a city 8.816101 35180
## 16 Alabama Mobile In a city 8.654895 36611
## 18 Alabama Montgomery In a city 10.219433 36110
## 20 Alabama Russell In a city 9.774144 31901
## 24 Alabama Walker In a city 8.140101 35501
## 26 Arizona Cochise In a city 5.266898 85626
## 27 Arizona Coconino In a city 5.108442 86011
## 28 Arizona Maricopa In a city 12.213530 85019
## 33 Arizona Mohave In a city 5.798753 86411
## 37 Arizona Pinal In a city 11.305553 85119
## 42 Arkansas Arkansas In a city 10.747275 72160
## 44 Arkansas Crittenden In a city 11.299191 72364
## 46 Arkansas Garland In a city 8.157882 71901
## 47 Arkansas Jackson In a city 13.111634 72112
## 48 Arkansas Phillips In a city 10.491194 72342
## 50 Arkansas Pope In a city 8.728574 72801
## 52 Arkansas Pulaski In a city 9.915562 72202
## 56 Arkansas Washington In a city 8.270712 72764
## 62 California Calaveras In a city 7.442024 95249
## 65 California Fresno In a city 6.460891 93726
## 68 California Humboldt In a city 3.263085 95501
## 69 California Humboldt In a city 3.263085 95501
## 72 California Imperial In a city 14.850927 92243
## 73 California Inyo In a city 3.513012 93530
## 78 California Kern In a city 5.747609 93304
## 80 California Lake In a city 3.106960 95453
## 81 California Los Angeles In a city 12.828002 91702
## 82 California Los Angeles In a city 9.270752 91502
## 89 California Los Angeles In a city 6.967713 90806
## 90 California Los Angeles In a city 7.908347 93534
## 92 California Merced In a city 6.814392 95348
## 93 California Monterey In a city 5.302320 93906
## 94 California Nevada In a city 6.999230 95945
## 96 California Orange In a city 9.799704 92804
## 99 California Plumas In a city 4.344805 95971
## 109 California San Benito In a city 6.855167 95045
## 110 California San Bernardino In a city 12.753308 91763
## 111 California San Bernardino In a city 9.821147 92395
## 114 California San Bernardino In a city 10.488258 92408
## 115 California San Diego In a city 7.145248 91911
## 117 California San Diego In a city 9.679734 92123
## 119 California San Diego In a city 9.679734 92113
## 129 California Santa Clara In a city 7.773003 95112
## 130 California Santa Cruz In a city 5.061995 95062
## 134 California Sonoma In a city 5.929803 95401
## 137 California Tulare In a city 6.584716 93292
## 139 California Ventura In a city 5.451476 93040
## 140 California Ventura In a city 6.898124 93063
## 144 Colorado Arapahoe In a city 4.573257 80120
## 148 Colorado Denver In a city 5.085550 80216
## 151 Colorado Elbert Not in a city 4.932027 80106
## 153 Colorado Larimer In a city 3.848796 80521
## 154 Colorado Mesa In a city 3.835828 81501
## 158 Connecticut Fairfield In a city 7.666388 6604
## 167 Connecticut New Haven In a city 8.597731 6513
## 172 Delaware Kent Not in a city 8.936072 19943
## 173 Delaware Kent In a city 8.936072 19901
## 175 Delaware New Castle Not in a city 9.750578 19701
## 177 Delaware New Castle In a city 10.006074 19801
## 180 District Of Columbia District of Columbia In a city 10.379364 20024
## 185 Florida Brevard In a city 7.032766 32901
## 193 Florida Hillsborough In a city 9.425858 33609
## 196 Florida Leon In a city 11.398163 32304
## 197 Florida Miami-Dade In a city 5.398935 33015
## 200 Florida Orange In a city 10.597919 32803
## 202 Florida Palm Beach In a city 9.015444 33430
## 209 Florida Sarasota In a city 7.769048 34231
## 213 Georgia Chatham In a city 8.573355 31408
## 217 Georgia Cobb In a city 8.932246 30144
## 219 Georgia DeKalb Not in a city 13.441151 30034
## 220 Georgia DeKalb In a city 13.441151 30341
## 230 Georgia Muscogee In a city 9.774144 31901
## 232 Georgia Muscogee In a city 11.067864 31903
## 233 Georgia Paulding Not in a city 8.926431 30153
## 236 Georgia Walker Not in a city 9.459333 30741
## 239 Idaho Ada Not in a city 4.777594 83642
## 240 Idaho Ada In a city 4.777594 83704
## 242 Idaho Benewah Not in a city 5.968979 83861
## 244 Idaho Lemhi Not in a city 1.629838 83467
## 245 Idaho Shoshone In a city 3.762413 83839
## 246 Illinois Adams In a city 7.209795 62301
## 247 Illinois Champaign In a city 9.847546 61820
## 253 Illinois Cook In a city 12.665990 60652
## 257 Illinois Cook In a city 12.665990 60501
## 261 Illinois DuPage In a city 10.858659 60540
## 264 Illinois Kane In a city 10.424477 60120
## 266 Illinois Lake In a city 11.318619 60096
## 268 Illinois McHenry In a city 9.594218 60013
## 273 Illinois Madison In a city 9.167594 62002
## 282 Illinois Will In a city 9.763484 60935
## 283 Illinois Winnebago In a city 8.366767 61104
## 287 Indiana Dubois Not in a city 10.056619 47546
## 288 Indiana Dubois In a city 10.056619 47546
## 290 Indiana Elkhart In a city 10.677485 46516
## 300 Indiana Lake In a city 16.909207 46402
## 301 Indiana Lake In a city 16.909207 46323
## 304 Indiana Madison In a city 10.074651 46016
## 311 Indiana St. Joseph In a city 10.677485 46615
## 312 Indiana St. Joseph In a city 10.677485 46615
## 314 Indiana Tippecanoe In a city 10.383808 47904
## 315 Indiana Vanderburgh In a city 10.353399 47708
## 316 Indiana Vanderburgh In a city 10.353399 47710
## 317 Indiana Vanderburgh In a city 10.353399 47714
## 320 Iowa Black Hawk In a city 7.031191 50703
## 322 Iowa Clinton In a city 8.476871 52732
## 323 Iowa Delaware Not in a city 7.055311 52038
## 325 Iowa Lee In a city 7.147876 52632
## 333 Iowa Pottawattamie In a city 7.267698 51510
## 336 Iowa Scott In a city 8.892743 52802
## 337 Iowa Van Buren Not in a city 6.775811 52565
## 339 Iowa Wright In a city 6.449447 50525
## 342 Kansas Linn Not in a city 7.091016 66075
## 351 Kentucky Boyd In a city 9.249667 41101
## 355 Kentucky Christian Not in a city 9.278928 42220
## 356 Kentucky Daviess Not in a city 11.770804 42303
## 359 Kentucky Franklin In a city 9.463386 40601
## 360 Kentucky Hardin In a city 9.101351 42701
## 366 Kentucky Kenton In a city 11.791236 41011
## 368 Kentucky Madison In a city 9.471734 40475
## 369 Kentucky Ohio Not in a city 9.213426 42320
## 370 Kentucky Pike In a city 9.442975 41501
## 373 Louisiana Calcasieu In a city 11.304278 70668
## 374 Louisiana Calcasieu In a city 13.328800 70601
## 376 Louisiana East Baton Rouge Not in a city 14.006751 70807
## 377 Louisiana Iberville Not in a city 15.346915 70721
## 385 Louisiana St. Bernard In a city 8.966612 70043
## 386 Louisiana Tangipahoa In a city 9.363830 70401
## 391 Maryland Baltimore In a city 9.634452 21030
## 392 Maryland Baltimore In a city 10.940985 21221
## 393 Maryland Cecil Not in a city 12.053545 21921
## 396 Maryland Prince George's In a city 12.503977 20710
## 401 Maryland Baltimore (City) In a city 10.940985 21215
## 406 Massachusetts Bristol In a city 7.373641 2724
## 407 Massachusetts Essex In a city 8.038855 1904
## 411 Massachusetts Hampden In a city 6.457219 1103
## 412 Massachusetts Hampden In a city 6.457219 1103
## 415 Massachusetts Suffolk In a city 8.038855 2215
## 416 Massachusetts Suffolk In a city 8.038855 2129
## 422 Michigan Bay In a city 8.184169 48708
## 423 Michigan Berrien In a city 7.698057 49038
## 425 Michigan Chippewa In a city 3.036035 49783
## 435 Michigan Missaukee Not in a city 4.696748 49667
## 436 Michigan Monroe In a city 9.638580 48133
## 437 Michigan Muskegon In a city 5.980675 49440
## 438 Michigan Oakland In a city 8.680140 48237
## 443 Michigan Wayne In a city 10.164487 48209
## 446 Michigan Wayne In a city 8.680140 48152
## 447 Michigan Wayne In a city 10.164487 48120
## 451 Minnesota Cass Not in a city 3.737680 56641
## 452 Minnesota Dakota In a city 7.112881 55124
## 453 Minnesota Hennepin In a city 7.985953 55423
## 458 Minnesota Olmsted In a city 6.979581 55902
## 464 Minnesota Saint Louis In a city 3.939519 55806
## 467 Minnesota Washington In a city 12.591723 55003
## 472 Mississippi Grenada In a city 7.575577 38901
## 473 Mississippi Harrison In a city 8.637199 39507
## 477 Mississippi Lauderdale In a city 9.436430 39307
## 483 Missouri Clay Not in a city 7.626603 64068
## 490 Missouri St. Louis City In a city 12.080511 63107
## 492 Missouri St. Louis City In a city 12.080511 63102
## 493 Montana Cascade In a city 2.399152 59401
## 494 Montana Flathead In a city 2.362930 59912
## 496 Montana Flathead In a city 2.379521 59901
## 503 Montana Missoula In a city 2.370427 59868
## 506 Montana Sanders In a city 2.695437 59873
## 507 Montana Silver Bow In a city 1.890419 59703
## 509 Nebraska Douglas In a city 7.034605 68105
## 514 Nebraska Scotts Bluff In a city 3.899386 69361
## 515 Nebraska Washington In a city 7.034605 68008
## 516 Nevada Clark In a city 9.264662 89101
## 517 Nevada Clark In a city 8.390542 89179
## 524 New Hampshire Merrimack In a city 4.662478 3275
## 525 New Hampshire Rockingham In a city 4.859331 3904
## 527 New Jersey Atlantic In a city 8.857962 8205
## 528 New Jersey Atlantic In a city 8.857962 8401
## 532 New Jersey Bergen In a city 11.619372 7074
## 533 New Jersey Camden In a city 12.116203 8104
## 535 New Jersey Essex In a city 7.874637 7108
## 536 New Jersey Gloucester In a city 12.116203 8027
## 549 New Jersey Warren In a city 7.818637 8865
## 550 New Mexico Bernalillo In a city 10.102199 87109
## 553 New Mexico Dona Ana Not in a city 7.813534 88063
## 555 New Mexico Grant In a city 5.302022 88022
## 556 New Mexico Lea In a city 5.869165 88242
## 558 New Mexico San Juan In a city 5.917382 87401
## 559 New Mexico Santa Fe In a city 6.046069 87505
## 566 New York Erie In a city 9.746405 14206
## 568 New York Essex Not in a city 3.385406 12997
## 575 New York Onondaga In a city 5.595432 13214
## 579 New York Richmond In a city 11.619372 10314
## 581 New York Steuben Not in a city 5.249962 14801
## 583 New York Westchester In a city 7.880172 10538
## 589 North Carolina Cumberland In a city 9.734499 28314
## 603 North Carolina Martin In a city 6.828791 27846
## 604 North Carolina Mecklenburg In a city 9.822602 28205
## 610 North Carolina Pitt In a city 7.947334 27858
## 611 North Carolina Pitt Not in a city 7.947334 27834
## 614 North Carolina Swain In a city 6.331454 28713
## 616 North Carolina Wake Not in a city 10.097656 27606
## 617 North Carolina Watauga In a city 6.417602 28607
## 619 North Dakota Billings Not in a city 2.756029 58622
## 621 North Dakota Cass Not in a city 4.833778 58102
## 622 North Dakota Mercer Not in a city 3.585468 58523
## 625 Ohio Butler In a city 9.781110 45015
## 626 Ohio Clark In a city 10.913352 45503
## 628 Ohio Cuyahoga In a city 10.372480 44113
## 630 Ohio Cuyahoga In a city 10.372480 44113
## 631 Ohio Cuyahoga In a city 10.372480 44127
## 633 Ohio Cuyahoga In a city 10.372480 44105
## 636 Ohio Franklin In a city 12.869319 43207
## 640 Ohio Hamilton In a city 11.791236 45216
## 650 Ohio Lorain In a city 8.595181 44054
## 652 Ohio Lucas In a city 11.184033 43605
## 653 Ohio Lucas In a city 11.184033 43614
## 655 Ohio Mahoning In a city 8.409051 44503
## 657 Ohio Montgomery In a city 10.668137 45402
## 662 Ohio Stark In a city 10.896422 44702
## 664 Ohio Summit In a city 10.896422 44320
## 669 Oklahoma Muskogee In a city 8.118360 74401
## 672 Oklahoma Ottawa In a city 7.169499 74360
## 674 Oklahoma Sequoyah In a city 7.784839 74962
## 677 Oregon Deschutes In a city 3.632990 97701
## 680 Oregon Jackson In a city 3.294533 97503
## 684 Oregon Lane In a city 4.409401 97404
## 686 Oregon Lane In a city 4.409401 97477
## 689 Oregon Multnomah In a city 5.704889 97206
## 690 Oregon Multnomah In a city 5.704889 97217
## 691 Oregon Umatilla In a city 2.716046 97801
## 692 Oregon Union In a city 2.314552 97850
## 693 Oregon Washington In a city 4.288868 97124
## 694 Pennsylvania Adams Not in a city 8.188808 17353
## 695 Pennsylvania Allegheny In a city 10.517943 15224
## 698 Pennsylvania Allegheny In a city 8.420333 15065
## 702 Pennsylvania Berks Not in a city 8.714116 19605
## 703 Pennsylvania Bucks In a city 9.168158 19007
## 704 Pennsylvania Cambria In a city 7.956593 15902
## 705 Pennsylvania Centre In a city 6.260422 16803
## 709 Pennsylvania Delaware In a city 10.092345 19013
## 710 Pennsylvania Erie In a city 6.858108 16511
## 711 Pennsylvania Lackawanna In a city 6.267811 18519
## 714 Pennsylvania Montgomery In a city 12.116203 19401
## 715 Pennsylvania Northampton In a city 7.818637 18020
## 718 Pennsylvania Philadelphia In a city 12.116203 19147
## 720 Pennsylvania Philadelphia In a city 12.116203 19123
## 722 Pennsylvania Washington In a city 8.132669 15301
## 723 Pennsylvania Washington Not in a city 9.569958 15021
## 725 Pennsylvania York In a city 10.066116 17403
## 728 Rhode Island Providence In a city 7.373641 2860
## 730 Rhode Island Providence In a city 7.373641 2916
## 734 South Carolina Edgefield Not in a city 9.171641 29847
## 735 South Carolina Florence Not in a city 9.566813 29501
## 741 South Carolina Richland In a city 10.209979 29147
## 749 South Dakota Jackson Not in a city 2.796456 57750
## 752 South Dakota Pennington In a city 3.111781 57702
## 753 South Dakota Pennington In a city 3.111781 57701
## 756 Tennessee Hamilton In a city 8.111289 37403
## 759 Texas Bowie In a city 8.901955 75501
## 762 Texas Dallas In a city 12.512475 75201
## 765 Texas Ector In a city 8.426707 79761
## 768 Texas El Paso In a city 7.315803 79905
## 769 Texas El Paso In a city 7.315803 79902
## 770 Texas Harris In a city 12.979706 77039
## 771 Texas Harris In a city 13.939939 77520
## 785 Utah Cache In a city 3.533593 84321
## 797 Utah Weber In a city 4.165049 84414
## 800 Vermont Chittenden In a city 5.117599 5401
## 805 Virginia Chesterfield In a city 8.145455 23234
## 807 Virginia Fairfax In a city 10.379364 22041
## 809 Virginia Frederick Not in a city 8.590233 22624
## 814 Virginia Rockingham Not in a city 6.754194 22802
## 815 Virginia Bristol City In a city 7.325804 24201
## 818 Virginia Norfolk City In a city 8.935473 23510
## 820 Virginia Roanoke City In a city 6.214850 24012
## 830 West Virginia Berkeley In a city 8.657932 25401
## 837 West Virginia Kanawha In a city 8.596698 25303
## 838 West Virginia Marion In a city 6.514602 26559
## 843 West Virginia Wood In a city 9.496352 26105
## 845 Wisconsin Brown In a city 6.938970 54302
## 851 Wisconsin La Crosse In a city 7.227094 54601
## 855 Wisconsin Milwaukee In a city 8.759594 53204
## 856 Wisconsin Milwaukee In a city 8.759594 53221
## 857 Wisconsin Milwaukee In a city 8.759594 53203
## 859 Wisconsin Ozaukee Not in a city 7.576285 53004
## 864 Wisconsin Waukesha In a city 8.759594 53186
## 870 Wyoming Laramie In a city 3.494108 82001
## 874 Wyoming Sublette In a city 2.377328 82941
## zcta_area zcta_pop ImpSrf_500m ImpSrf_1000m ImpSrf_5000m ImpSrf_10000m
## 3 16716984 9042 19.173010380 11.144896190 15.17861538 9.72538702
## 5 154069359 20045 16.493079580 11.096453290 14.67778782 8.22007849
## 6 162685124 30217 19.139273360 16.671496540 16.38548211 8.07396239
## 7 26929603 9010 41.833044980 41.032006920 36.00406256 23.17904127
## 8 166239542 16140 1.697653430 3.982392777 6.02132173 4.55677306
## 9 385566685 3699 0.000000000 0.029195502 0.13983563 0.14749677
## 14 230081840 13794 0.416089965 0.314878893 0.50526777 0.55436407
## 16 12304398 6106 4.415224913 7.137975779 10.88611101 8.61915436
## 18 76743399 12997 14.544982700 16.715830450 19.23057009 14.99611808
## 20 11526212 8638 60.453287200 49.143598620 25.78546970 16.35380485
## 24 129312323 10424 0.230968858 3.908520761 6.31025796 2.54823344
## 26 1907854 1021 2.191176471 6.884731834 21.31491739 34.12899364
## 27 1756314 6362 45.193771630 45.465353260 14.15930393 6.09215889
## 28 9712436 25042 42.699826990 47.867647060 46.96630799 42.57106226
## 33 1078691243 224 0.035467128 0.047577855 0.05337808 0.12004590
## 37 114040443 21219 9.136678201 6.527681661 4.72904838 5.19754611
## 42 870327339 10736 0.077854671 0.041738754 2.21037651 1.69591981
## 44 187833290 15831 19.891868510 19.809688580 7.01377240 5.83595233
## 46 267404865 29491 14.493944640 10.875648790 11.08313616 6.30600471
## 47 850468590 11467 0.121042831 0.438328466 1.33208764 1.45522827
## 48 231233559 5532 4.338447653 3.585860839 2.52292242 1.38636722
## 50 21061744 18685 34.968858130 30.480536330 12.05652210 4.89826289
## 52 22473183 9978 47.620242210 41.854831360 24.92685936 17.61485928
## 56 161282228 50230 37.698096890 35.264489620 19.88379077 9.23333752
## 62 217037743 4042 3.019400353 3.174809502 1.50897991 0.67654026
## 65 16619268 40705 49.064878890 45.014273360 43.17791961 40.84114189
## 68 18248993 23976 45.261245670 41.496539790 12.67844235 6.59641047
## 69 18248993 23976 43.503460210 39.262975780 12.62606878 6.79089928
## 72 285619001 48030 37.992214530 35.903330450 16.44853674 6.72002630
## 73 2362111 47 1.091695502 2.720588235 0.38475835 0.22145034
## 78 19670450 48731 41.965397920 43.215397920 32.53487838 24.70244922
## 80 216831467 11256 0.488754325 1.681228374 2.68808960 1.14045423
## 81 438640351 59705 39.298442910 40.727076120 33.99479185 31.15503477
## 82 3456372 11371 56.303633220 52.617647060 31.86506329 33.30081162
## 89 8908246 42399 52.006055360 54.985726640 47.85574061 44.81103720
## 90 45994950 39341 42.429065740 35.536764710 25.49895505 12.51693134
## 92 123033842 30805 34.037197230 29.309688580 18.68156800 7.08087441
## 93 33858896 59461 41.012975780 37.119593430 25.43141798 9.53441607
## 94 166352193 25199 12.400519030 21.561797750 7.28734735 2.30230859
## 96 18142648 85914 46.873702420 48.136031090 49.88297527 52.58511317
## 99 262028573 5970 0.000000000 0.000000000 2.00678758 0.69768383
## 109 122780227 4046 3.062283737 4.841695502 5.61140312 4.59982420
## 110 13393075 36375 25.130622840 23.440095160 38.03329100 30.35412187
## 111 44293390 42400 37.981833910 37.357482700 20.56785205 14.58713437
## 114 27507391 15271 31.811418690 31.540873700 33.83004052 29.40606209
## 115 30333860 82999 23.108131490 40.456314880 40.45845125 32.66974620
## 117 21148247 26823 56.965397920 42.598615920 29.75532551 29.90046699
## 119 13647793 56066 62.734429070 57.593209340 38.41929141 33.50956822
## 129 18370035 55927 48.653979240 49.518814880 51.53856937 47.14243190
## 130 13072397 36079 36.596885810 36.810121110 17.01702055 7.39119223
## 134 53417691 36981 40.013840830 45.027771610 29.79309986 13.05518246
## 137 281391266 39032 27.161764710 24.794766440 19.51055090 11.18669059
## 139 182830429 2031 0.000000000 0.216479239 0.65008581 0.98950352
## 140 85340849 54366 36.325259520 30.950043250 10.59012936 10.38812009
## 144 21504485 28435 17.936851210 18.384948100 23.58065516 24.38239927
## 148 27187751 10924 58.764705880 51.607050170 45.90796314 38.93462366
## 151 455241296 4232 0.030276817 0.081805683 0.08219550 0.09416226
## 153 36755388 33409 28.989619380 30.424524220 21.34565333 11.21059215
## 154 20575487 23465 40.743079580 35.850129760 22.69715372 13.29916864
## 158 8110911 30313 58.379188710 57.638163620 31.75553660 18.49529343
## 167 18730722 38978 40.617647060 38.991565740 27.19323488 18.95232940
## 172 170749870 11425 0.035467128 1.378027682 1.92242833 1.81347802
## 173 206602384 35055 11.153114190 12.690960210 16.04360884 7.06968069
## 175 68921004 39194 1.713667820 5.880190311 6.61051926 7.77507748
## 177 10721983 16286 59.957612460 57.729238750 28.55478956 18.12438490
## 180 6770280 11510 29.448096890 31.177768170 38.79731620 32.46569834
## 185 32565205 24521 26.134948100 24.370242210 23.13169848 15.96490833
## 193 11328513 15999 38.154844290 29.228157440 26.87878180 20.95286159
## 196 40585058 46145 10.228373700 16.650302770 11.81471189 8.57892309
## 197 15086374 63544 18.666089970 21.315743940 22.83548173 30.13365906
## 200 17970616 19020 50.255190310 40.442041520 22.26135632 21.54934951
## 202 294583151 21427 17.667820070 12.886461940 4.65205816 1.72431558
## 209 24426338 30268 33.910899650 30.267521760 28.59532348 19.65596908
## 213 63184987 9952 19.911764710 29.192906570 20.55866479 14.37787880
## 217 58464478 52252 17.013840830 20.422145330 24.00038390 17.76547799
## 219 43902603 43113 4.467128028 7.536548443 13.73060530 17.59956125
## 220 26345053 31793 58.557093430 52.203703700 29.76887236 23.77365453
## 230 11526212 8638 49.250865050 53.703211010 28.50136484 17.12696099
## 232 25938344 20128 29.800892860 27.150290310 19.73467637 14.57594160
## 233 308580504 18411 0.146384480 0.416884247 0.49060050 1.65611233
## 236 64531856 29290 11.711937720 17.971453290 15.03189631 13.60384630
## 239 106348819 37008 9.601489758 19.177488200 19.26077862 14.95269207
## 240 22687581 39628 23.794117650 21.334126300 19.24932782 15.37007519
## 242 1318545641 6360 11.706747400 8.108131488 2.18799974 0.75048427
## 244 1961914237 6270 17.817474050 19.039497720 3.83393784 1.50247752
## 245 280195894 1417 3.409169550 5.129757785 2.11807940 1.34892641
## 246 29259045 33758 25.121972320 29.919333910 23.63133935 8.49094662
## 247 16927476 36964 27.078719720 34.394679930 27.73157949 11.60978917
## 253 12993375 40959 46.833910030 47.886245670 50.49914999 47.63385206
## 257 12527100 11626 49.870242210 52.538927340 45.83907949 42.79024054
## 261 34163246 42910 31.802768170 28.413062280 29.54526159 26.84758845
## 264 42961291 50955 28.108996540 30.087370240 28.31867908 21.44739863
## 266 12430994 6897 5.591695502 6.378027682 7.25241204 7.43260487
## 268 37767656 26872 18.270761250 22.202205880 14.47040052 15.01401140
## 273 108835425 32704 22.087370240 17.847177600 17.52298883 10.53994784
## 282 48993151 1064 0.947231834 1.251081315 1.09572412 2.35888605
## 283 11681766 19269 56.359861590 49.163607160 32.54386376 21.99368266
## 287 320636133 21009 0.458477509 0.865268166 3.03455003 2.80712640
## 288 320636133 21009 0.088235294 1.780709343 5.99668166 2.30974833
## 290 45976445 33501 34.512975780 28.622621110 27.33282836 17.04582607
## 300 9847717 6726 37.387543250 41.630190310 31.43501187 19.59758001
## 301 16064128 22743 40.532871970 40.122837370 37.21530884 31.60950279
## 304 16488664 19225 51.568339100 39.806444640 19.12024682 7.24924343
## 311 8458861 14609 35.670415220 38.191392730 34.16067270 20.04923779
## 312 8458861 14609 38.437217710 34.165994620 32.50364071 23.07136730
## 314 14655846 16277 39.109861590 37.664576120 25.06006786 12.17476041
## 315 1435639 439 55.486159170 47.223955940 23.08192378 11.92787648
## 316 22768908 19527 40.732495510 25.678315410 18.39363921 12.45801656
## 317 20678502 34107 30.615051900 33.030709340 26.73357354 12.77969663
## 320 242656830 19863 40.174740480 37.206098620 20.06918965 10.25825075
## 322 287014236 28293 26.732698960 27.171280280 12.31863761 4.76933814
## 323 76144562 491 1.311418685 0.600346021 0.46300347 0.76589283
## 325 131951222 13086 20.064013840 20.261910140 7.04379061 3.30263736
## 333 4841212 3785 12.549307960 20.233131490 33.60171759 22.30026336
## 336 24767339 10868 49.388408300 34.868295850 23.09469553 16.95178243
## 337 300828566 2027 1.218858131 1.434472318 0.60403499 0.79411491
## 339 361364714 3583 1.934256055 1.706388763 0.99957645 1.06554288
## 342 272318335 2103 0.145328720 0.415224913 1.40623775 1.10297665
## 351 25001268 18758 19.322751320 25.684882420 13.29137244 8.80782487
## 355 458536402 6844 0.119377163 0.121323529 0.31772375 0.39331821
## 356 124084216 38909 24.178130510 21.174473070 12.16170016 5.76348142
## 359 571041492 49566 8.145328720 8.514057093 7.09401556 4.53095011
## 360 410836962 48194 3.064878893 3.926686851 7.20331270 3.78469670
## 366 18478742 25872 19.910899650 20.168468860 26.53989829 21.61805110
## 368 707062295 55803 3.820069204 6.380269058 10.27862458 4.32489624
## 369 381586639 8205 0.967128028 0.640570934 0.50639018 0.87882803
## 370 520591265 23298 2.179065744 1.340513291 4.49822737 2.69538339
## 373 372865121 6424 0.000000000 1.371107266 2.28199186 1.73192974
## 374 40115011 32384 48.088392860 41.085789710 24.90340853 16.07062570
## 376 66042672 20377 0.000000000 0.036115917 1.96952203 5.12165430
## 377 13156931 701 11.918685120 17.126313490 6.48197912 4.96691059
## 385 20138103 16760 38.389273360 44.414330920 20.51259799 19.69395433
## 386 90633544 20415 4.775951557 4.625114155 3.51360376 4.24781808
## 391 61585318 24355 12.407439450 17.341046710 11.07091506 6.19053821
## 392 38242330 42154 22.786332180 17.268598620 24.88631781 23.83206383
## 393 244939298 44471 6.294117647 4.479661792 1.02248243 2.73573092
## 396 2847143 9313 38.927335640 37.687283740 29.36109178 30.44613618
## 401 17645223 60161 39.589100350 36.682742210 20.15880813 24.94368303
## 406 4811066 16908 60.222318340 56.748269900 23.48564846 15.50719019
## 407 11708211 18203 47.345279720 44.997128340 27.19411279 27.68135136
## 411 1191989 2479 61.506920420 46.594506920 29.92467055 22.03787042
## 412 1191989 2479 43.147923880 43.379757790 29.05872549 21.69613871
## 415 1979040 26125 63.417820070 43.298875430 49.89165083 46.30339141
## 416 3492181 16439 54.667820070 54.516435990 53.99243478 39.89837470
## 422 72168561 27262 29.921480140 37.392100620 24.58664440 10.14453280
## 423 97692930 9310 0.253460208 1.498393021 3.80930716 1.99910069
## 425 391215697 19668 5.705139766 9.669023420 29.32675003 46.74988012
## 435 206211677 549 0.000000000 0.303411953 0.31569003 1.00571965
## 436 69548571 5753 8.287197232 5.802768166 5.29427767 8.81794163
## 437 1559681 953 59.462037040 46.976675720 23.30423924 12.34774037
## 438 13341572 29319 45.504325260 44.075908300 45.52838684 41.58751640
## 443 17501605 32262 56.887543250 50.811202420 60.72008696 65.56690887
## 446 31368242 31173 49.658304500 37.881271630 30.74426438 31.08843930
## 447 8253572 8274 47.708477510 45.179930800 53.38802669 57.32993676
## 451 81814324 324 0.000000000 0.574826990 0.44353509 0.36424634
## 452 43542873 49084 20.817474050 20.019247400 30.37561178 20.99731525
## 453 18071306 35375 25.883217990 28.282223180 36.29082324 30.51484765
## 458 105086001 21968 18.565529620 21.587182970 11.84365321 7.97311154
## 464 8688079 9576 29.085640140 27.634737320 18.63098602 11.50992628
## 467 5349029 3471 33.157439450 18.842560550 8.17597781 5.56893308
## 472 620295438 18403 0.000000000 0.000000000 0.39191975 1.28060367
## 473 20902323 16234 38.859861590 25.674524220 15.59475859 10.96870350
## 477 247178145 18603 13.012500000 14.785762110 15.72400513 6.61577682
## 483 223598466 36280 0.785467128 3.791089965 3.09264073 5.91620997
## 490 6218033 11912 47.232698960 48.236159170 39.40485826 34.91263627
## 492 3864674 2316 61.051038060 49.516652250 38.12130892 33.64095733
## 493 8047826 13774 33.866782010 35.738970590 23.02639318 8.54281570
## 494 363216876 13499 0.168685121 2.277198549 4.57507728 1.89050831
## 496 1170968819 49693 14.752595160 13.761029410 13.41852335 4.41895043
## 503 468659168 2054 0.096020761 0.123702422 0.31675801 0.30982238
## 506 1959419268 3085 0.000000000 0.002811419 1.05585536 0.54777240
## 507 2321651 43 2.211937716 3.246323529 8.08601111 4.89098155
## 509 9746876 23842 31.251730100 32.579152250 39.41728818 33.13224905
## 514 296157543 17999 0.643044619 0.418157720 6.94811058 4.05299326
## 515 343478275 12285 0.000000000 2.143166090 5.41246719 2.05544788
## 516 13863521 46055 54.475778550 52.873053630 52.13239708 45.91377220
## 517 308602532 2340 0.000000000 0.064878893 0.28506136 0.47489812
## 524 111629686 11437 32.492214530 23.395977510 5.34207262 4.86166555
## 525 29002836 7703 27.820909090 26.604461130 12.35462389 6.47581042
## 527 109829121 28626 0.000000000 0.205017301 2.75335850 5.67178188
## 528 27575373 39554 48.930795850 39.782871970 12.27793567 17.83438440
## 532 3249396 2708 37.771626300 27.090181660 36.17731901 40.32269093
## 533 8025452 23851 35.065743940 41.459775090 36.16105000 37.59802474
## 535 3550737 24386 48.269031140 48.479238750 47.86080484 40.00411532
## 536 19159930 4888 1.413494810 1.850562284 15.91524436 19.75612516
## 549 111455506 29840 27.691202870 35.971300450 15.00604088 8.29345748
## 550 26199125 40432 44.192906570 40.922577850 35.66467962 29.02427080
## 553 30049707 12799 25.074141050 27.923976610 42.19711408 46.05492436
## 555 30348270 1343 4.810553633 3.962586505 2.13695415 2.47425751
## 556 143281405 6141 0.101211073 0.181228374 3.34899868 4.63114265
## 558 337668408 46293 36.681660900 31.552119380 14.73705653 5.91020399
## 559 179321360 31013 21.469723180 23.222750870 10.96746173 4.03090315
## 566 12552705 20751 48.054770320 46.248012370 39.11000244 30.10980843
## 568 168962963 1239 0.000000000 0.000000000 0.79306357 0.31162365
## 575 9467889 8793 41.729238750 34.750000000 21.51738115 15.78192728
## 579 35520695 85510 25.558823530 23.576773360 30.81607444 28.94715320
## 581 281410630 5475 0.138408304 0.401234568 1.01791507 1.24879314
## 583 9309984 16597 33.695501730 24.477076120 19.09113594 18.39483336
## 589 59675644 54520 20.457612460 23.438148790 21.41623964 16.98352169
## 603 250829531 2988 0.000000000 0.012110727 0.42921539 0.31913902
## 604 30619956 43931 15.485294120 16.551038060 26.08404476 22.23774382
## 610 156028841 55781 24.707612460 29.713532110 18.69900454 7.77246134
## 611 458186307 52914 4.101211073 7.999773960 5.10204689 4.81433862
## 614 353434267 8686 0.597750865 2.273356401 1.78585794 0.83593435
## 616 64224192 43210 0.663494810 0.649282624 8.47979001 12.04196027
## 617 256402734 33059 0.255190311 3.757791017 5.22096348 1.94604284
## 619 1620564646 1489 0.262110727 0.418181818 0.48255245 0.61910150
## 621 80929583 29850 0.323529412 5.347318339 9.38256972 12.13475045
## 622 677769113 3619 0.000000000 0.075692042 2.03731288 1.33929855
## 625 15590702 12038 3.831314879 3.839033288 14.29326113 12.86259904
## 626 44911607 32999 35.237288140 35.692462090 20.93271964 7.73925779
## 628 9917299 19213 35.653114190 36.757785470 37.81626322 27.71148789
## 630 9917299 19213 35.107843140 39.477611940 40.63998056 28.20096186
## 631 4907885 5586 46.383217990 45.847318340 45.08983299 30.02071038
## 633 22690774 40089 34.087370240 42.450499550 39.11544620 31.10833559
## 636 60393119 45144 42.627162630 45.754974050 34.80759439 34.12072633
## 640 7809213 9570 42.342560550 39.453503460 27.75515496 23.46684271
## 650 32391850 12591 0.086505190 4.021626298 13.12886390 11.04197124
## 652 18251430 28346 30.133752240 29.082013050 29.85237131 21.49011843
## 653 23384827 29290 5.788062284 13.425821800 28.51656230 25.64503635
## 655 1164199 1021 42.779411760 40.377811420 25.80482224 17.26042273
## 657 10877473 11420 55.394463670 54.799091700 35.27212612 26.45648386
## 662 1391166 1053 62.717993080 54.771842560 31.62609810 20.06381224
## 664 25259624 19937 32.160034600 33.519247400 30.62145662 24.04891084
## 669 313459418 17476 12.293252600 16.082761770 12.49803802 6.31374335
## 672 16649433 31 1.304498270 1.859861592 4.08843456 3.50504235
## 674 371190228 5092 0.000000000 0.090614187 0.34412189 0.21483695
## 677 1758347878 58993 15.024221450 15.205017300 11.16425285 6.53944225
## 680 215917460 10528 0.732698962 0.697448097 0.69628607 1.58621435
## 684 25097986 32255 25.408304500 36.415224910 40.00348574 18.91566044
## 686 32304566 36874 52.923875430 36.724697230 24.91394923 16.21288364
## 689 16907018 47596 45.164359860 44.183607270 42.71369619 37.76600805
## 690 33471270 31438 43.634083040 48.153979240 48.74901612 34.56001049
## 691 1271940128 21521 0.007785467 0.049307958 0.52906657 1.82477775
## 692 749321189 16955 0.000000000 0.000000000 1.25694344 1.81434559
## 693 111580289 48349 31.408304500 29.559688580 17.43769784 13.76225710
## 694 105676905 3228 0.626919603 1.004416961 0.90069754 1.15790461
## 695 2607025 10141 54.356401380 52.773572660 39.05039708 28.00063488
## 698 32066841 11588 34.173875430 37.627595160 14.05701516 8.09733698
## 702 43379611 18985 14.649653980 15.076557090 21.89610866 12.38772820
## 703 17658290 21125 25.435121110 28.443339100 23.14566951 19.49087533
## 704 23599705 12513 36.851952770 31.008088070 9.04067306 4.54654969
## 705 62776845 23685 17.109861590 20.479671280 11.04040631 4.62349764
## 709 14821318 35130 42.651384080 38.810121110 21.30373047 16.67137626
## 710 19410241 11382 39.184256060 35.409602080 21.08966165 11.64578668
## 711 6236104 5104 23.437443740 26.943010000 21.67354768 11.89013597
## 714 15621931 41753 38.935121110 32.613754330 27.83334581 16.11738725
## 715 35827006 20447 19.053633220 21.514273360 20.58190920 14.23242042
## 718 3645140 36228 64.583910030 64.628460210 53.60086465 43.87893966
## 720 3273535 13416 64.089965400 53.248839780 51.93263109 42.79014690
## 722 315753777 49331 13.366782010 15.086072660 13.89575202 6.13844673
## 723 237040192 7352 0.000000000 1.049524221 1.44452893 1.55231362
## 725 52958231 39042 33.365051900 31.040224910 24.02216523 13.42528981
## 728 13980575 45199 59.179065740 61.768135900 38.99714373 29.43142924
## 730 6617778 8139 39.049307960 36.256704150 35.28742869 29.46420836
## 734 235303530 5480 0.482698962 0.408307556 1.39586396 1.13004150
## 735 139086997 43220 1.416089965 1.373486159 5.11840378 5.67463024
## 741 485430 0 25.112456750 22.423442910 17.25105757 15.58111183
## 749 712688135 295 0.000000000 0.000000000 0.26997692 0.43943190
## 752 756278009 32980 0.000000000 4.737889273 10.38823143 9.00811799
## 753 183417086 43462 1.942041522 9.246107266 15.92454565 9.77667534
## 756 4434309 5811 43.178200690 43.856185120 26.79740091 17.07131105
## 759 246569347 36298 29.891868510 37.812614260 28.36576935 13.21582337
## 762 3741301 9409 60.365051900 60.769896190 43.07342937 31.05095730
## 765 26391291 30488 22.198961940 19.377595160 15.94506795 9.90912311
## 768 16787888 26036 53.430795850 50.623702420 54.15424540 65.04523327
## 769 16985318 21236 53.450692040 63.633650520 74.59777248 71.37556353
## 770 26147028 27562 37.952422150 37.410899650 28.22177943 31.01069189
## 771 62010184 36489 8.458110517 10.727734810 14.37849526 19.45717385
## 785 498609538 44074 33.029411760 34.046280280 17.88197856 7.31151662
## 797 42184001 26507 20.562283740 17.152622140 19.12264992 10.51072761
## 800 15341366 28185 55.167820070 47.033520760 16.03710092 9.44831705
## 805 51093298 42989 20.584775090 22.556228370 15.43643929 13.42534887
## 807 7243208 27989 12.509515570 10.858996540 21.99952285 24.07428186
## 809 49949933 2565 3.649653979 3.101427336 2.07007112 2.56703557
## 814 234379912 26293 13.983564010 8.523572664 3.57977232 4.97063624
## 815 26832166 17116 9.323529412 12.229671280 12.69131597 5.29865565
## 818 2913325 7031 36.705882350 31.008434260 29.15117968 27.64334564
## 820 52860333 29015 40.211937720 31.171496540 31.41074373 20.29348914
## 830 15187751 14622 26.725778550 32.888408300 12.11484819 5.15565596
## 837 9121644 7112 49.307958480 42.595804500 15.54955983 8.79474982
## 838 1582041 1269 4.194636678 5.360294118 7.88305846 3.48446500
## 843 32418567 12333 3.287197232 8.205666090 6.74325841 6.29888518
## 845 24737298 30611 40.673010380 45.753892730 26.05790814 19.29988369
## 851 190482955 49282 36.752595160 24.439446370 9.58675200 5.02022493
## 855 8503810 42355 58.782871970 52.443987890 38.52252586 32.00897185
## 856 23848404 37701 46.866782010 45.835773820 37.89054231 27.69192056
## 857 1156887 938 62.370242210 60.968641870 40.84958732 30.58771465
## 859 91486480 3330 11.407574390 8.941670349 1.97438356 1.26329962
## 864 32557513 33592 36.352941180 34.586505190 25.08783328 16.01545722
## 870 152691897 35484 55.339965400 48.729671280 23.50255203 8.25109415
## 874 1123566431 5028 0.252595156 0.187716263 0.68594922 0.38343661
## ImpSrf_15000m CountyArea_m2 CountyPop Log_Dist_PrscRd Log_PriRdLen_5000m
## 3 5.2472094 1534877333 54428 5.760131 8.517193
## 5 5.1612102 1385618994 104430 5.261457 9.066563
## 6 4.7401296 1501737720 101547 7.112373 8.517193
## 7 17.4524844 2878192209 658466 6.600958 11.156977
## 8 4.3006226 2878192209 658466 6.526896 10.915066
## 9 0.1623359 3423328940 194656 9.789557 8.517193
## 14 0.9481135 2878192209 658466 8.734821 8.517193
## 16 7.7170777 3184223082 412992 5.779170 10.364272
## 18 10.9185636 2031190777 229363 7.187640 9.623936
## 20 11.1153413 560435436 189885 5.522936 8.517193
## 24 1.5108669 2049177420 67023 7.041732 8.517193
## 26 42.5553742 15969063883 131346 6.760903 8.517193
## 27 3.2333337 48222689085 134421 6.314788 10.446760
## 28 36.4662683 23828260196 3817117 7.134575 11.187228
## 33 0.1884870 34475546921 200186 9.193844 8.517193
## 37 5.0320363 13896869603 375770 7.845170 8.517193
## 42 0.9595542 1678203880 8715 7.562635 8.517193
## 44 3.6957129 1579271661 50902 5.019187 10.228978
## 46 3.6517172 1755444878 96024 3.289128 8.517193
## 47 0.9000045 1641892684 17997 8.526809 8.517193
## 48 0.7755042 1801758335 21757 7.261019 8.543367
## 50 2.6748149 2104490517 61754 5.096611 9.608993
## 52 13.3991611 1967777404 382748 7.337567 11.421452
## 56 7.1199758 2439681852 203065 6.204129 10.024998
## 62 0.6945969 2641819725 45578 5.623082 8.517193
## 65 23.4995826 15431126407 930450 7.016310 10.296547
## 68 17.6496768 9241044673 134623 6.281180 8.517193
## 69 18.7331710 9241044673 134623 6.370899 8.517193
## 72 4.6401254 10817352524 174528 6.291994 9.460040
## 73 0.2480639 26368354217 18546 6.351006 8.517193
## 78 18.0927628 21061565098 839631 6.568373 8.517193
## 80 0.9530985 3254227528 64665 6.966199 8.517193
## 81 26.0856675 10509869649 9818605 6.041234 10.988538
## 82 30.6315743 10509869649 9818605 6.344698 11.313636
## 89 48.0262563 10509869649 9818605 3.903602 10.539447
## 90 7.8642331 10509869649 9818605 6.867299 10.440055
## 92 4.4558917 5011554741 255793 7.645542 8.517193
## 93 6.6301676 8496702738 415057 7.879342 8.517193
## 94 1.3974245 2480616988 98764 6.717025 10.906160
## 96 48.3694125 2047561084 3010232 7.250344 10.716579
## 99 0.4163350 6612350713 20007 7.962802 8.529855
## 109 2.6584082 3596742302 55269 6.337232 8.517193
## 110 25.2949114 51947229509 2035210 7.680300 10.511916
## 111 10.0724312 51947229509 2035210 6.588919 10.404811
## 114 21.2022833 51947229509 2035210 7.857653 10.500586
## 115 39.7339731 10895120648 3095313 7.744937 11.490633
## 117 31.0346420 10895120648 3095313 5.889929 11.779896
## 119 33.2061175 10895120648 3095313 6.439666 11.573368
## 129 35.4485778 3341343470 1781642 6.801589 11.286887
## 130 7.1697730 1152986019 262382 6.450574 10.237285
## 134 8.4932836 4081430136 483878 5.987186 10.115648
## 137 6.7016981 12494658169 442179 6.656609 9.702769
## 139 0.8185847 4773691889 823318 8.052788 8.517193
## 140 11.7225874 4773691889 823318 6.070393 9.095626
## 144 22.0811319 2067070049 572003 5.201886 9.578615
## 148 33.6251198 396268588 600158 5.811724 11.513138
## 151 0.1490909 4793671177 23086 8.280334 8.517193
## 153 5.9575519 6723613486 299630 6.318517 8.517193
## 154 6.9294753 8622002815 146723 -1.462256 9.677926
## 158 11.9566578 1618456437 916829 2.886065 10.867445
## 167 12.6638734 1565663096 862477 6.716950 11.349773
## 172 2.2404895 1518196116 162310 6.357293 8.517193
## 173 3.9705766 1518196116 162310 2.214658 8.530172
## 175 8.1856657 1104074798 538479 6.529542 8.517193
## 177 13.0534610 1104074798 538479 4.560810 11.361322
## 180 26.1544088 158114680 601723 5.939102 11.448810
## 185 13.9570750 2630557912 543376 6.862799 9.280769
## 193 17.8455963 2642341101 1229226 4.791074 10.696502
## 196 5.6703336 1727138746 275487 5.098285 10.334628
## 197 25.8214977 4915061046 2496435 6.369778 10.764004
## 200 22.7603321 2339870128 1145956 4.538834 9.726365
## 202 1.0193743 5101663125 1320134 7.674137 8.517193
## 209 21.4043266 1439690708 379448 7.700367 9.497963
## 213 9.8684097 1104465640 265128 6.238530 11.621291
## 217 15.5336897 879428364 688078 7.202209 11.443959
## 219 21.1543939 693033963 691893 6.932028 11.348749
## 220 21.5227386 693033963 691893 4.563606 11.375301
## 230 10.9352231 560435436 189885 6.267272 10.228193
## 232 10.5070682 560435436 189885 7.030366 10.758131
## 233 1.3624822 803754838 41475 8.638483 8.517193
## 236 10.5055549 1156117093 68756 6.632640 9.374314
## 239 10.1356848 2726158067 392365 6.829740 10.837257
## 240 9.6645057 2726158067 392365 5.425693 9.559563
## 242 0.4608820 2011429331 9285 5.213907 8.517193
## 244 0.7811719 11819118494 7936 5.851135 8.517193
## 245 0.8126662 6810799882 12765 7.611572 10.608780
## 246 4.7797337 2214963578 67103 6.690377 9.751087
## 247 6.4100834 2580316235 201081 6.537995 10.333022
## 253 43.2251298 2448383588 5194675 6.627345 8.538217
## 257 41.1653946 2448383588 5194675 6.029633 9.786552
## 261 27.8268133 848218379 916924 7.789542 9.334756
## 264 17.4038568 1346943286 515269 6.624206 10.041881
## 266 9.0950386 1149099822 703462 7.469536 8.517193
## 268 14.3815032 1562206475 308760 7.244290 8.533759
## 273 6.9998911 1853347576 269282 4.826338 8.721351
## 282 2.3677934 1752271023 113449 6.023027 8.517193
## 283 12.3990013 1329601795 295266 4.223333 8.662116
## 287 1.6052756 1106622334 41889 7.706197 8.517193
## 288 1.5762059 1106622334 41889 7.699416 8.517193
## 290 10.8393664 1199605640 197559 6.514821 9.047706
## 300 20.4009781 1292303671 496005 6.435126 10.951465
## 301 27.6881849 1292303671 496005 4.976504 10.908938
## 304 4.2766561 1170454727 131636 6.048040 8.517193
## 311 13.1383248 1185826798 266931 5.059614 10.305430
## 312 13.7884643 1185826798 266931 6.709637 10.878554
## 314 6.0514130 1294491529 172780 5.641494 9.888527
## 315 7.2238476 604697995 179703 6.334829 9.363844
## 316 6.8800866 604697995 179703 7.317122 8.517193
## 317 7.6343847 604697995 179703 5.680478 9.763274
## 320 6.2011954 1465333940 131090 5.191200 9.400834
## 322 2.7482083 1799821282 49116 6.597922 8.517193
## 323 0.9325887 1496381153 17764 8.575773 8.517193
## 325 2.2959440 1340366450 35862 4.874397 8.517193
## 333 17.0784755 2461213949 93158 5.789315 10.837193
## 336 12.8303747 1186444226 165224 6.148184 10.172046
## 337 0.6457308 1255597645 7570 5.925072 8.517193
## 339 0.8429762 1503286817 13229 4.827232 8.517193
## 342 0.7338603 1538604962 9656 6.720172 8.517193
## 351 6.9316705 414046208 49542 6.779779 8.517193
## 355 0.5649423 969940558 12460 5.926369 8.517193
## 356 2.9858240 1187111257 96656 4.386866 8.803877
## 359 2.4779218 538061269 49285 7.363009 8.517193
## 360 2.2686828 1614282063 105543 6.553245 10.750597
## 366 18.8490590 415044980 159720 2.068241 11.143917
## 368 2.8225914 1132583005 82916 5.530689 10.133252
## 369 0.8348565 1521032417 23842 7.571922 10.085873
## 370 2.1134106 2037887589 65024 5.206763 8.517193
## 373 1.3900614 2754864273 192768 6.604687 9.957431
## 374 11.1352129 2754864273 192768 5.179874 10.849479
## 376 6.5071486 1179412087 440171 7.928563 8.517193
## 377 5.8065642 1602236448 33387 3.680138 8.517193
## 385 18.3268725 977764266 35897 5.175603 8.517193
## 386 3.2868471 2049391733 121097 5.141601 9.331428
## 391 7.1514304 1549594703 805029 6.640054 10.307858
## 392 19.8086392 1549594703 805029 5.449489 11.151308
## 393 5.2072349 896842957 101108 4.736126 8.517193
## 396 24.2990266 1250163582 863420 3.621094 10.868872
## 401 20.9250383 209643241 620961 4.896128 10.610362
## 406 10.1507458 1432510950 548285 5.779904 10.575000
## 407 24.2175840 1275732137 743159 6.634540 8.517193
## 411 16.0214142 1598385896 463490 6.110227 10.909299
## 412 16.0650640 1598385896 463490 4.095634 10.874658
## 415 33.7395510 150618894 722023 4.052612 11.628408
## 416 32.4947065 150618894 722023 4.133293 11.620994
## 422 6.9206539 1145557748 107771 5.943211 10.565608
## 423 2.1805585 1470457874 156813 7.970059 10.558056
## 425 49.0478254 4036289692 38520 4.673200 9.998064
## 435 0.8847985 1462634072 14849 6.732059 8.517193
## 436 10.8535405 1422924260 152021 4.466086 10.687363
## 437 7.1178740 1293040828 172188 2.338427 9.888806
## 438 42.3871151 2247237402 1202362 7.343914 10.077511
## 443 66.1560786 1585280173 1820584 3.510144 11.366125
## 446 28.4267861 1585280173 1820584 4.567776 10.732978
## 447 61.2271585 1585280173 1820584 7.543749 11.316717
## 451 0.2705767 5235770376 28567 9.474679 8.517193
## 452 19.2670326 1456007312 398552 6.745190 10.538180
## 453 31.1318223 1433793826 1152425 7.221479 10.762975
## 458 5.1352471 1692165422 144248 3.282982 8.517193
## 464 6.8143105 16180692391 200226 4.932965 10.579533
## 467 4.5603860 995285532 238136 6.233762 8.517193
## 472 1.2241451 1093251406 21906 8.239864 8.517193
## 473 7.4764868 1486637454 187105 7.277701 8.517193
## 477 3.4363088 1822402881 80261 7.256593 11.065319
## 483 4.9243190 1028998027 221939 6.346836 9.958925
## 490 27.7034412 160343174 319294 5.792187 11.549682
## 492 27.0335476 160343174 319294 5.882251 11.523917
## 493 4.2360897 6988196577 81327 6.234598 11.493261
## 494 1.7277821 13176979222 90928 7.007866 8.517193
## 496 2.6308519 13176979222 90928 6.977756 8.517193
## 503 0.1992169 6716936093 109299 7.416969 8.517193
## 506 0.3147926 7149723773 11413 8.176879 8.517193
## 507 2.5540739 1860847940 34200 8.231527 9.891906
## 509 24.5115349 850693384 517110 7.308713 10.944594
## 514 2.3013205 1915039277 36970 7.923347 8.517193
## 515 1.3912386 1009986004 20234 7.003711 8.517193
## 516 37.1244420 20438710673 1951269 6.568149 11.624229
## 517 0.3665037 20438710673 1951269 8.116774 9.609929
## 524 5.9903979 2419349805 146445 5.707519 10.458903
## 525 6.4510628 2565935077 197131 6.597414 10.636316
## 527 6.4208385 1439267859 274549 7.396768 8.517193
## 528 31.3378703 1439267859 274549 5.380933 8.537774
## 532 39.9056636 603490137 905116 7.129030 10.648950
## 533 34.2897117 573067842 513657 5.647454 11.430740
## 535 33.4017446 326888224 783969 6.802030 10.292121
## 536 20.8496994 833989655 288288 7.354081 10.831652
## 549 5.8453931 924412063 108692 6.458374 10.268131
## 550 18.7818373 3006530549 662564 6.740597 11.289973
## 553 50.1760883 9861408558 209233 6.319924 11.290904
## 555 1.5165570 10260561575 29514 5.479758 8.517193
## 556 2.5977102 11372460346 64727 8.025371 8.517193
## 558 3.2649140 14278776370 130044 6.319245 8.517193
## 559 2.1811098 4945359181 144170 3.955202 10.208422
## 566 31.4682807 2700563452 919040 2.033161 10.698671
## 568 0.2952681 4647029522 39370 7.527404 8.517193
## 575 11.1193452 2016019983 467026 6.045674 10.584822
## 579 30.1856124 151178512 468730 7.115295 10.713175
## 581 1.2347673 3601531651 98990 6.766147 8.517193
## 583 18.1393361 1114982482 949113 4.888892 11.368267
## 589 11.2669557 1689487711 319431 6.396219 8.517193
## 603 0.7951191 1194547357 24505 7.330811 8.517193
## 604 19.3984931 1356745512 919628 7.895353 9.649576
## 610 4.0005421 1688606138 168148 6.003402 8.517193
## 611 3.7024405 1688606138 168148 6.421012 8.517193
## 614 0.6278083 1367504099 13981 6.540096 8.517193
## 616 11.1067230 2163206258 900993 7.765304 10.910740
## 617 1.1147087 809515805 51079 6.679800 8.517193
## 619 0.5222704 2975515808 783 7.998508 9.908590
## 621 8.9737945 4571166077 149778 6.343578 10.712244
## 622 0.8102143 2701246664 8424 8.528949 8.517193
## 625 11.4947054 1209668470 368130 5.573650 8.517193
## 626 4.2150080 1029450254 138333 5.717090 9.685788
## 628 22.5139117 1184118946 1280122 4.358720 10.993587
## 630 23.6167378 1184118946 1280122 4.545377 10.932851
## 631 24.7167389 1184118946 1280122 4.193005 10.667788
## 633 25.0819147 1184118946 1280122 5.605433 10.815928
## 636 28.1989591 1378361040 1163414 5.803574 11.309341
## 640 22.1925327 1051302780 802374 3.761575 9.865007
## 650 9.4258476 1271946706 301356 7.150964 10.139685
## 652 16.0404544 882810658 441815 7.399141 10.262591
## 653 20.1662478 882810658 441815 6.528500 10.848474
## 655 11.9461181 1066098928 238823 5.207158 10.641838
## 657 19.8395391 1195417263 535153 5.062413 9.925069
## 662 13.0778375 1489943930 375586 4.508541 10.474530
## 664 16.7195140 1069012672 541781 5.397366 10.691963
## 669 3.3205987 2099056912 70990 5.823957 8.517193
## 672 2.2828962 1219421293 31848 6.023053 8.517193
## 674 0.6252810 1743764515 42391 7.559153 8.517193
## 677 3.5945574 7817065156 157733 7.700556 8.517193
## 680 2.2387589 7209356169 203206 7.081952 8.517193
## 684 10.8042325 11792524334 351715 7.072527 9.516444
## 686 11.0243489 11792524334 351715 3.018341 11.047425
## 689 32.1846828 1117054236 735334 6.955998 10.865651
## 690 33.5792844 1117054236 735334 6.480461 11.353199
## 691 1.1149661 8328130471 75889 8.364663 8.517193
## 692 1.0565744 5274784008 25748 8.406553 10.308810
## 693 10.8916348 1875746999 529710 7.633282 9.797037
## 694 1.5639484 1343342705 101407 6.651112 8.517193
## 695 24.3285715 1890884266 1223348 6.281415 11.352714
## 698 6.8466145 1890884266 1223348 6.385141 8.517193
## 702 7.9386019 2218341544 411442 6.821359 9.131517
## 703 18.5056999 1565148677 625249 2.527939 11.182035
## 704 2.5280683 1782819861 143679 6.605884 8.517193
## 705 2.8301026 2874682925 153990 6.093232 8.556108
## 709 15.2377814 476152313 558979 -0.167176 10.615420
## 710 6.8671794 2069799485 280566 5.151823 8.517193
## 711 7.0492390 1189006298 214437 6.906396 10.427861
## 714 15.0272907 1251067046 799874 6.657300 11.574515
## 715 12.7636067 957444154 297735 8.077430 9.789158
## 718 34.3071819 347321129 1526006 0.244920 11.872358
## 720 34.6113784 347321129 1526006 4.687935 11.640534
## 722 3.8878286 2219591093 207820 4.915074 10.269291
## 723 2.4607657 2219591093 207820 6.600319 8.708258
## 725 8.1922143 2341819225 434972 5.987619 10.362150
## 728 22.1386381 1060604571 626667 4.847115 10.852575
## 730 23.2928852 1060604571 626667 6.252943 10.989115
## 734 0.8158585 1296046259 26985 7.537304 8.517193
## 735 4.1582521 2071897847 136885 7.576233 9.532329
## 741 12.9793081 1960797690 384504 5.744930 10.101940
## 749 0.4193461 4827514117 3031 7.209915 8.517193
## 752 5.3391622 7191239757 100948 6.673186 10.826625
## 753 5.2631775 7191239757 100948 6.555430 8.600202
## 756 12.1054110 1404890184 336463 5.431576 10.443126
## 759 7.1990980 2292153748 92565 6.109818 10.409153
## 762 29.2631330 2256602704 2368139 5.425112 12.013498
## 765 5.4005623 2324999033 137130 6.060328 10.139866
## 768 60.5900483 2622862905 800647 4.368874 11.099817
## 769 68.5181921 2622862905 800647 3.968919 11.148788
## 770 30.7030179 4411986582 4092459 7.008461 8.517193
## 771 19.2594764 4411986582 4092459 7.134172 10.645648
## 785 4.3352668 3016851000 112656 5.390011 8.517193
## 797 8.0839814 1492050864 231236 3.827340 10.478768
## 800 5.0652291 1389731195 156545 5.755803 10.831303
## 805 12.5720262 1096334108 316236 4.942640 10.667855
## 807 21.2887388 1012604465 1081726 5.187663 10.515353
## 809 3.3632614 1070949703 78305 5.621719 10.012115
## 814 3.7874110 2199121564 76314 4.996663 10.132054
## 815 3.2261085 33703512 17835 7.123645 10.952743
## 818 20.4729928 140171293 242803 4.034064 10.757877
## 820 11.2394281 110234185 97032 4.681545 10.733698
## 830 3.4414421 831754462 104169 1.645892 9.595364
## 837 6.0661987 2335100448 193063 5.373161 10.235822
## 838 2.1121107 799620921 56418 5.997960 9.530316
## 843 5.2323572 948607826 86956 6.119572 8.517193
## 845 11.6350822 1371938244 248007 5.421107 10.174230
## 851 4.2529938 1169862598 114638 5.535058 8.517193
## 855 23.7479441 625228895 947735 5.332814 11.217841
## 856 22.2725708 625228895 947735 6.196465 11.126329
## 857 24.2936125 625228895 947735 5.755181 11.224327
## 859 1.7568425 603666538 86395 5.058056 10.728035
## 864 14.5017723 1423388982 389891 4.394461 10.271998
## 870 4.0382790 6956479515 91738 6.023511 11.014742
## 874 0.2601246 12656072085 10247 7.927885 8.517193
## Log_PriRdLen_10000m Log_PriRdLen_15000m Log_PriRdLen_25000m
## 3 9.274303 9.658899 10.15769
## 5 11.137360 11.587258 12.01356
## 6 9.210340 9.615805 10.12663
## 7 11.849658 12.401245 12.98762
## 8 11.728685 12.230208 12.86472
## 9 9.210340 9.615805 10.12663
## 14 10.205823 11.380790 11.85793
## 16 11.116022 11.706608 12.23758
## 18 11.134147 11.608096 12.12632
## 20 11.363701 11.657322 12.05099
## 24 9.210340 9.615805 10.12663
## 26 9.210340 9.615805 10.12663
## 27 11.322142 11.986295 12.59834
## 28 12.041584 12.487677 12.95395
## 33 9.210340 9.615805 10.12663
## 37 9.246966 9.640371 10.14144
## 42 9.210340 9.615805 10.12663
## 44 11.043768 11.538634 12.42227
## 46 9.267750 9.693229 11.10778
## 47 10.609678 11.097203 11.67865
## 48 9.223513 9.624606 10.15902
## 50 10.352943 10.765148 11.40833
## 52 12.437953 12.938158 13.36950
## 56 11.816822 12.183208 12.48763
## 62 9.210340 9.615805 10.15508
## 65 11.196586 11.484608 11.79973
## 68 9.210340 9.615805 10.12663
## 69 9.210340 9.615805 10.12663
## 72 10.272648 10.699374 11.30947
## 73 9.210340 9.615805 10.18209
## 78 9.211135 9.616335 11.43024
## 80 9.210340 9.615805 10.56825
## 81 12.141433 12.631789 13.31298
## 82 12.766742 13.311711 14.13706
## 89 11.854460 12.799036 13.68739
## 90 11.185359 11.505800 12.07592
## 92 9.210340 9.615805 10.13391
## 93 9.210340 9.615805 10.22256
## 94 11.091275 11.250175 11.77927
## 96 11.422561 12.444049 13.41317
## 99 9.216691 9.620044 10.67846
## 109 9.210340 10.506685 11.17724
## 110 11.700147 12.291710 12.98691
## 111 11.052332 11.441351 12.11731
## 114 11.641090 12.093382 12.94328
## 115 12.399015 12.910645 13.52133
## 117 12.846330 13.491999 13.88062
## 119 12.797699 13.251158 13.79533
## 129 12.326560 12.808731 13.17874
## 130 10.970173 11.263167 11.66891
## 134 10.825729 11.284782 11.90145
## 137 10.670001 11.302436 11.64735
## 139 9.210340 9.615805 12.06101
## 140 10.438968 11.805347 13.34354
## 144 10.812050 12.437657 13.28737
## 148 12.552370 12.990455 13.66067
## 151 9.210340 9.615805 12.46996
## 153 11.072707 11.691640 12.27618
## 154 11.263470 11.735927 12.37850
## 158 12.070255 12.515037 12.95021
## 167 12.019183 12.425254 13.05034
## 172 9.210340 9.615805 10.13448
## 173 9.216851 9.620151 10.12924
## 175 10.677785 11.508043 12.50463
## 177 12.157118 12.432969 12.85808
## 180 12.409525 12.958102 13.76492
## 185 10.309049 10.742060 11.37495
## 193 11.597292 12.016302 13.04943
## 196 11.120939 11.521223 12.08251
## 197 11.322010 11.717280 12.79773
## 200 10.961388 11.740006 12.73101
## 202 9.210340 9.615805 10.12663
## 209 10.903851 11.513795 12.24009
## 213 12.034500 12.377475 12.84753
## 217 12.066111 12.400894 13.07402
## 219 12.275288 13.205841 13.84881
## 220 12.132928 12.528570 13.61554
## 230 11.396026 11.694368 12.08202
## 232 11.261575 11.543111 11.95726
## 233 9.210340 9.615805 10.61557
## 236 11.232785 11.884424 12.66412
## 239 11.642560 12.093105 12.71227
## 240 11.292305 11.964427 12.64087
## 242 9.210340 9.615805 10.12663
## 244 9.210340 9.615805 10.12663
## 245 11.339410 11.710576 12.21412
## 246 10.761122 11.097103 11.54471
## 247 11.547405 11.986278 12.50362
## 253 11.427997 12.194246 13.11483
## 257 11.047782 11.938442 13.15084
## 261 10.883949 11.810373 12.62948
## 264 10.671999 11.107670 12.07335
## 266 9.212138 11.490604 12.29050
## 268 9.250029 9.642439 11.70844
## 273 9.317621 9.688589 11.85080
## 282 10.395377 11.104044 11.68757
## 283 11.092882 11.828117 12.49342
## 287 9.210340 9.615805 11.52577
## 288 9.210340 9.615805 10.95215
## 290 11.571122 12.221943 12.87292
## 300 11.724020 12.143894 12.76039
## 301 11.760029 12.251795 12.93350
## 304 11.454928 11.676556 12.05415
## 311 12.046281 12.621617 13.02347
## 312 12.148892 12.688977 13.05605
## 314 10.779736 11.209038 11.64144
## 315 10.575723 11.092540 12.00841
## 316 10.412932 11.226282 12.04149
## 317 10.869420 11.143294 12.01866
## 320 10.759259 11.163602 11.53158
## 322 9.210340 9.615805 12.02065
## 323 9.210340 9.615805 10.73998
## 325 9.210340 9.615805 10.12663
## 333 11.995105 12.581973 13.14398
## 336 11.391961 12.131141 12.63965
## 337 9.210340 9.615805 10.12663
## 339 9.210340 10.468232 11.43152
## 342 9.210340 9.615805 10.12663
## 351 10.150854 11.061244 11.72126
## 355 9.210340 9.615805 10.12663
## 356 10.424235 11.140943 11.82204
## 359 10.571323 11.115314 11.84480
## 360 11.454810 11.855892 12.34785
## 366 12.055525 12.448545 12.98373
## 368 10.827304 11.263362 11.82508
## 369 11.280882 11.990761 12.85894
## 370 9.210340 9.615805 10.12663
## 373 10.766999 11.194631 11.88625
## 374 11.576119 11.793016 12.13647
## 376 9.500309 10.807520 11.61099
## 377 10.136412 11.005550 11.74554
## 385 10.900331 11.836608 12.22581
## 386 10.256561 11.385934 12.05991
## 391 11.509571 12.089549 13.15763
## 392 12.222729 12.744955 13.58204
## 393 11.078797 11.704318 12.39066
## 396 12.441358 13.100395 13.72929
## 401 12.086384 13.060478 13.71012
## 406 11.361398 11.718646 12.38569
## 407 10.755527 12.132764 13.15280
## 411 12.070491 12.420370 12.90297
## 412 12.057138 12.411431 12.90038
## 415 12.284280 13.006444 13.66248
## 416 12.163419 12.512073 13.64783
## 422 11.535059 12.053943 12.60304
## 423 11.729217 12.161154 12.58800
## 425 10.517277 10.857828 11.33351
## 435 9.210340 9.615805 11.05070
## 436 11.332178 11.892980 12.75759
## 437 10.930895 11.408769 11.88747
## 438 11.749469 12.641576 13.57649
## 443 12.348770 12.792937 13.38908
## 446 11.784169 12.182629 13.08026
## 447 12.426446 12.839899 13.47436
## 451 9.210340 9.615805 10.12663
## 452 11.231269 11.981318 12.95254
## 453 11.727568 12.588594 13.41797
## 458 10.519504 11.077042 11.66377
## 464 11.039108 11.255082 11.50839
## 467 10.730253 11.476555 12.52940
## 472 10.165964 11.229363 11.76538
## 473 10.666481 11.373478 11.90933
## 477 11.638982 11.944382 12.37128
## 483 11.023920 11.959875 12.80838
## 490 12.287657 13.067936 13.75681
## 492 12.287250 13.069321 13.75567
## 493 12.208436 12.483380 12.89152
## 494 9.210340 9.615805 10.12663
## 496 9.210340 9.615805 10.12663
## 503 9.210340 9.615805 10.12663
## 506 9.210340 9.615805 10.12663
## 507 11.576213 11.973141 12.62754
## 509 11.950032 12.697286 13.09864
## 514 9.210340 9.615805 10.30027
## 515 9.210340 11.187835 12.30055
## 516 12.417507 12.969315 13.52672
## 517 10.705289 11.164123 11.80332
## 524 11.447336 12.316280 12.92260
## 525 11.515213 12.044336 12.63783
## 527 9.210340 9.622713 10.17055
## 528 9.220684 9.622713 10.17055
## 532 11.781419 12.594654 13.65254
## 533 12.482345 12.887383 13.47823
## 535 11.761297 12.393022 13.32740
## 536 11.942171 12.643546 13.46986
## 549 11.056371 11.406268 11.86995
## 550 12.118298 12.505068 13.05595
## 553 12.044936 12.398740 12.82375
## 555 9.210340 9.615805 10.12663
## 556 9.210340 9.615805 10.12663
## 558 9.252573 9.670061 10.23859
## 559 11.375976 11.927421 12.66838
## 566 11.851505 12.311486 12.87774
## 568 9.210340 9.615805 10.12663
## 575 12.111553 12.512126 12.96163
## 579 11.776145 12.393307 13.19763
## 581 11.305164 12.276983 12.86996
## 583 12.598658 13.226603 13.88979
## 589 10.741996 11.821364 12.35355
## 603 9.210340 9.646777 10.14533
## 604 11.793185 12.581573 13.40365
## 610 9.210340 9.615805 10.12989
## 611 9.210340 9.615805 10.12989
## 614 9.210340 9.615805 10.12980
## 616 11.918997 12.426475 12.88330
## 617 9.210340 9.632637 10.23905
## 619 11.021073 11.502965 12.05331
## 621 11.655740 12.125090 12.63919
## 622 9.210340 9.615805 10.12663
## 625 9.842891 10.783059 12.42644
## 626 10.852901 11.326243 11.98754
## 628 11.985496 12.429279 13.37594
## 630 12.016720 12.429171 13.37649
## 631 12.024774 12.616430 13.38223
## 633 11.978597 12.656250 13.47358
## 636 12.642039 13.161784 13.61298
## 640 11.311574 12.159000 13.00342
## 650 11.584185 12.316694 12.88201
## 652 11.464039 12.402120 13.17270
## 653 12.354734 12.722396 13.16415
## 655 11.731803 12.469426 13.13328
## 657 10.899391 11.695319 12.34466
## 662 11.018451 11.377836 11.77874
## 664 11.804338 12.310307 13.17999
## 669 11.021256 11.344803 11.72831
## 672 10.607691 11.119978 11.69044
## 674 9.210340 10.414421 11.57796
## 677 9.210340 9.615805 10.12663
## 680 9.210340 10.871421 11.85357
## 684 11.194140 11.622989 12.08762
## 686 11.516061 11.777533 12.16175
## 689 12.548681 13.121125 13.53814
## 690 12.438884 13.040450 13.52361
## 691 10.857355 11.489329 12.33231
## 692 11.821355 12.308295 12.76296
## 693 10.968626 11.358781 12.52438
## 694 9.210340 9.615805 11.47320
## 695 12.221658 12.875175 13.49012
## 698 9.210340 11.307367 12.47596
## 702 9.765258 10.468650 12.25520
## 703 12.150971 12.702763 13.27617
## 704 9.812215 10.146395 10.47726
## 705 9.229987 9.634950 11.76042
## 709 11.857942 12.533980 13.54580
## 710 10.860349 11.497962 12.25638
## 711 11.873269 12.284037 12.71930
## 714 12.520181 12.891484 13.51160
## 715 10.836990 11.415239 12.49414
## 718 12.535338 12.919327 13.72243
## 720 12.468940 12.899789 13.79158
## 722 10.963037 11.401467 12.20775
## 723 9.359044 9.717356 11.85485
## 725 10.771717 11.051243 11.53006
## 728 12.178103 12.745765 13.26996
## 730 12.093354 12.705687 13.23851
## 734 9.210340 9.615805 11.60374
## 735 10.751894 11.136604 11.70463
## 741 10.924146 11.470145 12.52285
## 749 10.435528 11.044472 11.68015
## 752 11.623030 12.041045 12.54846
## 753 11.005037 11.681622 12.37392
## 756 11.053602 11.615405 12.62244
## 759 10.911749 11.226903 11.66023
## 762 12.646347 13.103172 13.89057
## 765 10.916242 11.410619 12.00912
## 768 11.871891 12.289860 12.81947
## 769 11.879212 12.285470 12.79035
## 770 11.443001 12.638809 13.57742
## 771 11.414800 11.862607 12.86719
## 785 9.210340 9.616890 11.15956
## 797 11.406619 11.837234 12.43098
## 800 11.510515 11.896597 12.66703
## 805 11.726471 12.433155 13.16133
## 807 12.487088 13.101585 13.55403
## 809 10.692505 11.071809 11.56201
## 814 10.838870 11.245473 11.86624
## 815 11.477252 11.767294 12.16223
## 818 11.812035 12.592103 13.03949
## 820 11.925001 12.516983 12.92051
## 830 10.341123 10.741298 12.32899
## 837 11.733183 12.389761 12.88941
## 838 10.762602 11.172123 11.94087
## 843 10.820432 11.517647 12.11044
## 845 10.852852 11.114485 11.48581
## 851 9.210340 10.736408 11.65673
## 855 12.037502 12.504575 12.96672
## 856 12.107686 12.513956 12.92005
## 857 11.996550 12.525460 12.98440
## 859 11.389017 11.701285 12.25433
## 864 11.212421 12.175898 13.14080
## 870 11.906102 12.324131 12.78463
## 874 9.210340 9.615805 10.12663
## Log_PrscRdLen_500m Log_PrscRdLen_1000m Log_PrscRdLen_5000m
## 3 8.611945 9.735569 11.770407
## 5 8.740680 9.627898 11.728889
## 6 6.214608 7.600902 12.298627
## 7 6.214608 9.075921 12.281645
## 8 6.214608 8.949239 11.039001
## 9 6.214608 7.600902 8.517193
## 14 6.214608 7.600902 8.517193
## 16 7.595229 8.658654 11.748979
## 18 6.214608 7.600902 11.473810
## 20 8.320842 9.324509 12.265658
## 24 6.214608 7.600902 11.263953
## 26 6.214608 8.196272 10.578712
## 27 6.214608 9.563727 11.642077
## 28 6.214608 7.600902 11.524040
## 33 6.214608 7.600902 8.517193
## 37 6.214608 7.600902 10.525636
## 42 6.214608 7.600902 10.420835
## 44 7.274460 8.810625 10.731814
## 46 7.823183 8.708900 11.002084
## 47 6.214608 7.600902 8.517193
## 48 6.214608 7.600902 10.450983
## 50 7.775346 8.689372 10.978451
## 52 6.214608 7.600902 12.062040
## 56 6.214608 8.928575 11.407706
## 62 7.617172 8.708766 10.116661
## 65 6.214608 7.600902 10.737947
## 68 6.214608 9.352865 11.226185
## 69 6.214608 9.327359 11.175748
## 72 6.214608 8.551344 10.430496
## 73 6.214608 8.195890 9.617658
## 78 6.214608 8.477044 10.959837
## 80 6.214608 7.600902 10.512377
## 81 7.670002 9.256363 11.441191
## 82 6.214608 9.262183 11.492556
## 89 7.816724 8.696727 11.258118
## 90 6.214608 7.852470 11.108810
## 92 6.214608 7.600902 11.130320
## 93 6.214608 7.600902 11.016846
## 94 6.214608 9.053811 11.302746
## 96 6.214608 7.600902 11.332527
## 99 6.214608 7.600902 10.179840
## 109 6.214608 8.698558 11.236743
## 110 6.214608 7.600902 10.874393
## 111 6.214608 8.543798 10.884304
## 114 6.214608 7.600902 10.670897
## 115 6.214608 7.600902 11.654502
## 117 7.925588 9.681314 11.866923
## 119 6.214608 8.828726 11.717448
## 129 6.214608 7.993257 11.575105
## 130 6.214608 8.555506 10.690128
## 134 8.160745 9.251122 10.867117
## 137 6.214608 8.662137 11.000477
## 139 6.214608 7.600902 10.545898
## 140 7.663066 9.161012 10.818938
## 144 7.796059 8.701215 10.844964
## 148 8.178463 9.590114 12.118715
## 151 6.214608 7.600902 9.151503
## 153 6.214608 8.783157 10.758653
## 154 8.136272 9.300562 11.276146
## 158 8.853998 9.899072 11.843622
## 167 6.214608 9.037500 12.154056
## 172 6.214608 9.198363 11.341830
## 173 7.820293 9.422886 12.316827
## 175 6.214608 9.226355 11.826570
## 177 9.182474 10.471049 12.690103
## 180 8.148503 9.552943 12.070162
## 185 6.214608 8.088255 11.387856
## 193 8.491501 9.302532 11.984032
## 196 8.111997 9.327349 11.757618
## 197 6.214608 8.607165 11.646377
## 200 7.807065 8.693256 12.014368
## 202 6.214608 7.600902 10.959465
## 209 6.214608 7.600902 11.172902
## 213 6.214608 9.988227 12.242222
## 217 6.214608 7.600902 12.028914
## 219 6.214608 7.600902 11.511094
## 220 8.643868 9.630551 11.912788
## 230 6.214608 9.582491 12.420900
## 232 6.214608 7.600902 11.998750
## 233 6.214608 7.600902 8.517193
## 236 6.214608 7.826166 11.756870
## 239 6.214608 8.621097 11.090403
## 240 8.068897 9.046820 10.962943
## 242 7.849256 8.882426 10.668350
## 244 7.434724 8.604901 10.604485
## 245 6.214608 7.600902 10.608780
## 246 6.214608 8.367262 11.624364
## 247 6.214608 8.473831 11.579432
## 253 6.214608 7.652655 10.951940
## 257 7.824192 9.299264 11.171908
## 261 6.214608 7.600902 10.556261
## 264 6.214608 8.667757 11.452380
## 266 6.214608 7.600902 10.352121
## 268 6.214608 7.600902 10.876699
## 273 8.126889 9.233145 11.595505
## 282 7.375690 8.418383 10.596130
## 283 8.820987 9.593653 11.385535
## 287 6.214608 7.600902 10.125906
## 288 6.214608 7.600902 11.001669
## 290 6.214608 8.048253 11.470999
## 300 6.214608 8.873185 11.611654
## 301 7.789508 8.690555 11.552441
## 304 7.323708 8.624370 11.636138
## 311 8.131450 8.958110 11.376859
## 312 6.214608 8.415227 11.577732
## 314 8.224503 9.378968 11.897445
## 315 6.214608 9.030428 11.752648
## 316 6.214608 7.600902 11.586764
## 317 8.247282 9.680957 11.689477
## 320 7.986986 8.920757 10.893800
## 322 6.214608 8.591997 11.286961
## 323 6.214608 7.600902 8.517193
## 325 8.122912 8.985838 10.949190
## 333 7.576519 8.642548 11.469740
## 336 7.017561 8.636803 11.642114
## 337 7.491485 8.643617 10.218471
## 339 7.289407 8.287900 10.364311
## 342 6.214608 8.333096 10.742540
## 351 6.214608 8.307334 11.661978
## 355 7.484374 8.675520 11.206761
## 356 8.530520 9.282196 11.662360
## 359 6.214608 7.600902 11.535209
## 360 6.214608 8.916350 12.085583
## 366 8.462708 9.944601 12.478702
## 368 7.783940 9.074932 11.682053
## 369 6.214608 7.600902 10.659500
## 370 7.762536 8.709596 11.691687
## 373 6.214608 8.115973 10.661254
## 374 8.114764 9.350786 11.753441
## 376 6.214608 7.600902 10.184258
## 377 8.011141 8.854809 11.119268
## 385 8.771385 10.024988 11.961131
## 386 7.850262 8.815132 11.217313
## 391 6.214608 8.746785 11.291652
## 392 8.043635 8.988393 12.446925
## 393 8.342167 9.215580 11.053371
## 396 9.117435 9.937107 12.574762
## 401 7.788901 9.099535 11.870329
## 406 8.162205 9.411620 11.866787
## 407 6.214608 8.703114 11.659422
## 411 7.651525 9.722432 12.051265
## 412 8.063083 9.851383 12.062444
## 415 9.398941 10.300221 12.717436
## 416 9.238158 10.268803 12.753989
## 422 7.588236 9.209463 11.616289
## 423 6.214608 7.600902 10.559596
## 425 7.830034 8.716201 10.361974
## 435 6.214608 8.285786 10.111080
## 436 8.913038 9.656781 11.520931
## 437 8.427734 9.242513 11.233202
## 438 6.214608 7.600902 11.886506
## 443 8.408451 9.217838 11.651474
## 446 8.416727 9.224756 11.071337
## 447 6.214608 7.600902 11.749633
## 451 6.214608 7.600902 8.517193
## 452 6.214608 8.247823 11.324304
## 453 6.214608 7.600902 11.207507
## 458 8.824927 9.753283 11.640244
## 464 7.763714 8.945571 11.152768
## 467 6.214608 8.644313 11.443257
## 472 6.214608 7.600902 10.233957
## 473 6.214608 7.600902 10.958889
## 477 6.214608 7.600902 11.750488
## 483 6.214608 9.071623 11.070838
## 490 7.879125 8.947560 11.986073
## 492 7.813023 8.944636 11.986190
## 493 6.214608 9.042515 11.642805
## 494 6.214608 7.600902 10.499668
## 496 6.214608 7.600902 10.790088
## 503 6.214608 7.600902 9.567612
## 506 6.214608 7.600902 9.743786
## 507 6.214608 7.600902 10.461036
## 509 6.214608 7.600902 11.674129
## 514 6.214608 7.600902 10.598166
## 515 6.214608 7.600902 10.604133
## 516 6.214608 9.235699 11.879678
## 517 6.214608 7.600902 10.151737
## 524 8.286215 9.309368 11.414491
## 525 6.214608 9.003960 11.989251
## 527 6.214608 7.600902 10.102998
## 528 8.272062 9.329666 10.996953
## 532 6.214608 7.600902 11.736244
## 533 7.542227 9.347415 12.274247
## 535 6.214608 7.881845 11.502853
## 536 6.214608 7.600902 11.735916
## 549 6.214608 9.199847 11.621114
## 550 6.214608 8.762115 11.582077
## 553 6.214608 8.934498 12.040456
## 555 8.047037 8.960496 10.524734
## 556 6.214608 7.600902 10.711752
## 558 6.214608 8.868397 11.083305
## 559 7.307603 9.161106 11.967532
## 566 7.680437 9.227205 11.962533
## 568 6.214608 7.600902 10.614843
## 575 7.826517 9.174146 11.855074
## 579 6.214608 7.600902 10.719272
## 581 6.214608 8.300403 10.714141
## 583 8.410050 9.356312 11.804939
## 589 6.214608 8.723354 11.039598
## 603 6.214608 7.600902 10.215591
## 604 6.214608 7.600902 11.175055
## 610 7.528257 8.902233 11.557770
## 611 6.214608 9.223907 11.193537
## 614 6.214608 9.247300 11.065914
## 616 6.214608 7.600902 11.435145
## 617 6.214608 8.219160 11.175044
## 619 6.214608 7.600902 9.908590
## 621 6.214608 9.015546 11.179665
## 622 6.214608 7.600902 8.517193
## 625 7.667096 8.661394 11.125750
## 626 8.298073 9.669868 11.689238
## 628 8.667917 9.677794 12.175904
## 630 7.302825 8.877051 12.178056
## 631 8.353743 9.297309 12.074814
## 633 7.248993 8.305587 11.942889
## 636 8.377937 9.444863 12.110319
## 640 8.377199 9.219266 11.630438
## 650 6.214608 7.600902 11.424097
## 652 6.214608 7.600902 11.218783
## 653 6.214608 8.480793 11.897678
## 655 8.543611 9.726839 12.005059
## 657 8.806184 9.911366 11.649747
## 662 8.637076 9.887257 12.038669
## 664 7.727003 8.848025 11.903284
## 669 8.465155 9.487232 11.760764
## 672 7.696905 9.430757 11.302542
## 674 6.214608 7.600902 9.906404
## 677 6.214608 7.600902 11.082701
## 680 6.214608 7.600902 10.044644
## 684 6.214608 7.600902 11.353244
## 686 8.404319 9.213970 11.537854
## 689 6.214608 7.600902 11.631461
## 690 6.214608 9.342903 12.029255
## 691 6.214608 7.600902 9.766293
## 692 6.214608 7.600902 10.506180
## 693 6.214608 7.600902 10.751246
## 694 6.214608 8.550292 10.362523
## 695 6.214608 8.406927 12.220567
## 698 6.214608 8.504138 11.323900
## 702 6.214608 8.116056 11.367252
## 703 8.314647 9.426870 11.951923
## 704 6.214608 8.730921 11.576639
## 705 7.211047 8.584375 11.499355
## 709 8.181549 9.475985 12.018824
## 710 7.770578 9.534857 11.518720
## 711 6.214608 7.600902 12.121454
## 714 6.214608 8.974067 12.111504
## 715 6.214608 7.600902 11.046177
## 718 8.776020 9.846123 12.755944
## 720 8.958326 10.140847 12.606617
## 722 7.244709 8.733907 11.448419
## 723 6.214608 8.108716 10.432981
## 725 7.427104 9.169983 11.932281
## 728 9.059406 9.952497 12.228315
## 730 6.214608 9.077843 12.176568
## 734 6.214608 7.600902 10.778400
## 735 6.214608 7.600902 10.348225
## 741 7.686731 9.241761 11.361600
## 749 6.214608 7.600902 9.667118
## 752 6.214608 8.829316 11.483796
## 753 6.214608 8.536069 11.590320
## 756 8.419697 9.802817 12.350174
## 759 7.169272 8.707020 11.870327
## 762 8.610477 9.546120 12.482512
## 765 7.409318 8.704054 11.350943
## 768 9.107094 10.400889 12.412546
## 769 9.248851 10.081137 12.078749
## 770 6.214608 7.600902 11.517496
## 771 6.214608 7.600902 11.174929
## 785 7.237732 8.280305 10.971330
## 797 8.152622 8.983696 11.737318
## 800 7.792083 9.186338 11.651509
## 805 8.356087 9.901216 11.912306
## 807 8.214226 9.297925 12.232018
## 809 7.653885 9.056176 10.596603
## 814 8.213868 9.167589 11.870266
## 815 6.214608 7.600902 11.880538
## 818 8.822938 9.899348 12.557693
## 820 8.387881 9.536369 12.456182
## 830 8.158263 9.421355 11.287198
## 837 7.705129 9.569466 12.016619
## 838 6.714515 8.267743 11.116330
## 843 7.165778 9.061611 10.963839
## 845 8.064728 9.500226 11.769954
## 851 8.507625 9.374385 11.320729
## 855 8.967770 9.755895 12.019523
## 856 6.897241 9.758872 11.821033
## 857 8.738359 9.872736 11.999244
## 859 8.557831 9.705652 11.416048
## 864 8.276997 9.653574 11.663286
## 870 7.391010 9.541845 11.831020
## 874 6.214608 7.600902 10.044448
## Log_PrscRdLen_10000m Log_PrscRdLen_15000m Log_PrscRdLen_25000m
## 3 12.840663 13.282656 13.79973
## 5 12.768279 13.189489 13.70026
## 6 12.994141 13.326132 13.85550
## 7 13.278416 13.826365 14.45221
## 8 12.128956 12.753045 13.80017
## 9 9.210340 9.615805 11.89377
## 14 10.420770 12.447722 13.34258
## 16 12.793503 13.281388 13.90072
## 18 12.818512 13.529346 14.10779
## 20 13.232356 13.659840 14.16990
## 24 12.120303 12.521557 13.10635
## 26 11.078097 11.400922 11.82078
## 27 12.390361 12.833780 13.33215
## 28 12.571092 13.213701 13.98699
## 33 9.336948 11.488893 12.11156
## 37 11.705446 12.322732 13.07762
## 42 11.230948 11.666007 12.22026
## 44 11.665526 12.147130 13.64745
## 46 12.204560 12.613562 13.38595
## 47 11.813696 12.213245 12.89298
## 48 11.228268 11.622009 12.64868
## 50 11.711572 12.157880 12.79400
## 52 13.127664 13.657267 14.10463
## 56 12.671614 13.325126 14.07803
## 62 11.049508 11.589992 12.52286
## 65 11.872009 12.302382 12.82163
## 68 11.962187 12.324057 12.85726
## 69 11.861234 12.267776 12.84934
## 72 11.499419 12.141250 13.11922
## 73 10.426984 11.272335 11.94941
## 78 12.193627 12.778735 13.42418
## 80 11.332983 11.915687 12.91542
## 81 12.381661 13.176847 14.19641
## 82 12.999874 13.680282 14.55411
## 89 12.615322 13.474215 14.43659
## 90 11.979379 12.607655 13.38056
## 92 11.873460 12.301434 12.73350
## 93 11.877927 12.563350 13.41588
## 94 11.796774 12.082217 12.55280
## 96 12.799125 13.647049 14.55346
## 99 11.194554 11.730875 12.40230
## 109 12.030751 12.688832 13.30387
## 110 12.627674 13.241419 13.94904
## 111 11.719320 12.138484 12.80342
## 114 11.980769 12.621793 13.53385
## 115 12.624065 13.130622 13.80250
## 117 12.931223 13.599764 14.19471
## 119 12.947196 13.457149 14.00775
## 129 12.596568 13.088190 13.63351
## 130 11.545490 12.012443 12.81410
## 134 11.615201 12.202486 13.11685
## 137 11.883233 12.511229 13.54333
## 139 11.532789 12.138566 13.34646
## 140 11.831047 12.727254 14.25077
## 144 12.409875 13.367230 14.26384
## 148 13.228694 13.839998 14.53036
## 151 10.178253 10.879724 13.01219
## 153 12.220826 12.768768 13.51108
## 154 12.310276 12.721813 13.15811
## 158 12.855318 13.407554 14.22322
## 167 13.190655 13.729250 14.44591
## 172 12.547750 13.302724 14.22158
## 173 13.109153 13.485700 14.08622
## 175 13.187858 13.936505 14.66419
## 177 13.657943 14.143572 14.84688
## 180 13.443043 14.333883 15.19792
## 185 12.613307 12.944777 13.38756
## 193 13.065977 13.574380 14.39669
## 196 12.953848 13.561059 14.27278
## 197 12.784542 13.486986 14.51925
## 200 13.284082 13.896006 14.56871
## 202 11.976625 12.467087 13.09711
## 209 12.295222 12.783223 13.73201
## 213 12.914183 13.384650 13.92079
## 217 13.076403 13.738936 14.58740
## 219 13.169062 14.122864 14.91974
## 220 13.180961 13.873859 14.85812
## 230 13.261553 13.684255 14.17879
## 232 13.060568 13.628275 14.15806
## 233 11.552674 12.323959 13.71120
## 236 13.254755 13.869858 14.56772
## 239 12.414234 12.950974 13.58425
## 240 12.352258 12.809081 13.49297
## 242 11.449951 11.985101 12.69291
## 244 11.326019 11.768369 12.42904
## 245 11.375845 11.737190 12.38742
## 246 12.320260 12.832933 13.57439
## 247 12.547662 12.932230 13.54257
## 253 12.482756 13.404255 14.29918
## 257 12.749302 13.446711 14.41914
## 261 12.114637 13.083833 14.15194
## 264 12.646684 13.209735 14.11713
## 266 11.855375 13.022636 14.06432
## 268 12.461055 13.360672 14.37529
## 273 12.648903 13.134540 13.91820
## 282 12.193886 12.863351 13.62368
## 283 12.343388 12.875070 13.64377
## 287 11.976564 12.681672 13.71448
## 288 11.898468 12.588908 13.46292
## 290 12.520660 13.156319 13.85618
## 300 12.534265 13.205146 13.97430
## 301 12.597040 13.292692 14.10324
## 304 12.576959 13.126360 13.92959
## 311 12.624121 13.189335 14.07243
## 312 12.667272 13.361839 14.05098
## 314 12.739730 13.099067 13.83358
## 315 12.467386 12.942689 13.72632
## 316 12.505198 12.920867 13.70190
## 317 12.480464 12.978102 13.72977
## 320 12.101734 12.674713 13.30423
## 322 12.054315 12.379172 13.30145
## 323 11.394630 12.181386 13.00992
## 325 11.832841 12.280232 12.99728
## 333 12.713839 13.231469 13.81055
## 336 12.677323 13.295432 13.86343
## 337 11.093419 11.607871 12.37246
## 339 11.446388 12.054521 12.81268
## 342 11.662769 12.242168 12.87832
## 351 12.828455 13.397908 14.16385
## 355 12.598644 13.406854 14.43261
## 356 12.853435 13.403536 14.09011
## 359 12.831974 13.302575 14.36063
## 360 13.090214 13.718563 14.52956
## 366 13.386773 13.959105 14.68766
## 368 12.742200 13.386567 14.26234
## 369 12.088944 13.099351 14.17295
## 370 12.660454 13.213372 14.02693
## 373 11.648163 12.006030 13.13966
## 374 12.713573 13.158706 13.72241
## 376 12.666601 13.552381 14.36943
## 377 12.435674 13.411729 14.38637
## 385 13.078701 13.704341 14.29293
## 386 12.459316 13.177539 14.03566
## 391 12.612522 13.486569 14.64136
## 392 13.344755 13.929591 14.75419
## 393 12.661076 13.671651 14.70095
## 396 13.720582 14.325754 15.14823
## 401 13.441016 14.102809 14.94211
## 406 12.896150 13.358457 14.25255
## 407 13.126566 13.866523 14.63702
## 411 13.188202 13.758879 14.43622
## 412 13.180634 13.763941 14.43533
## 415 13.622968 14.169571 14.87127
## 416 13.636922 13.998345 14.85746
## 422 12.467890 12.933698 13.71740
## 423 11.972975 12.440311 13.20817
## 425 10.950715 11.573969 12.22174
## 435 11.353358 11.793238 12.64301
## 436 12.165358 12.837804 13.90726
## 437 11.984902 12.315719 12.90438
## 438 13.119888 13.804053 14.60415
## 443 13.087633 13.676048 14.36676
## 446 12.253257 13.080016 14.19278
## 447 13.182091 13.752096 14.41791
## 451 9.210340 10.656574 11.84227
## 452 12.239378 12.914635 14.13566
## 453 12.616892 13.575661 14.46998
## 458 12.514470 12.966044 13.47354
## 464 12.135537 12.589146 13.05928
## 467 12.552776 13.101886 13.99563
## 472 11.790517 12.539836 13.20103
## 473 12.071597 12.628957 13.30374
## 477 12.610815 13.093836 13.65360
## 483 12.305707 12.974017 13.97700
## 490 12.983360 13.732622 14.56616
## 492 12.982494 13.734761 14.57069
## 493 12.312096 12.628101 13.03606
## 494 11.274264 11.749993 12.49141
## 496 11.474185 11.989702 12.64158
## 503 10.322881 10.755419 11.28567
## 506 10.602660 10.927311 11.36142
## 507 11.893005 12.310445 12.88635
## 509 12.691763 13.390932 13.92494
## 514 12.117148 12.563978 13.21650
## 515 11.264148 12.091956 13.29667
## 516 12.756283 13.291929 13.94893
## 517 11.112111 11.497788 12.38057
## 524 12.435616 13.410041 14.26639
## 525 12.763746 13.235896 14.10300
## 527 12.086833 12.571509 13.19456
## 528 11.815511 12.461983 13.02276
## 532 12.947144 13.694138 14.53733
## 533 13.655658 14.280043 14.95895
## 535 12.902111 13.570488 14.42257
## 536 13.071761 13.935895 14.94461
## 549 12.424062 12.971354 13.85667
## 550 12.962662 13.444690 14.08184
## 553 12.799174 13.457413 14.06260
## 555 11.768228 12.153963 12.75413
## 556 11.820714 12.223747 12.91691
## 558 11.835272 12.168106 12.94270
## 559 12.784925 13.157780 13.58953
## 566 13.129726 13.705625 14.29693
## 568 11.077503 11.584326 12.71212
## 575 13.115564 13.594612 14.25804
## 579 12.217010 13.231509 14.15861
## 581 12.033516 12.908484 13.53435
## 583 13.099758 13.858593 14.64987
## 589 12.425173 13.040119 13.71174
## 603 10.930441 12.281763 13.20098
## 604 12.676964 13.474203 14.33804
## 610 12.363808 12.758289 13.47495
## 611 12.172329 12.770499 13.56817
## 614 11.891498 12.425143 13.22865
## 616 12.780212 13.398637 14.05539
## 617 11.992792 12.430266 13.28358
## 619 11.247002 11.865000 12.43463
## 621 12.392315 12.846759 13.30080
## 622 10.880877 11.627899 12.30162
## 625 12.684551 13.296598 14.24642
## 626 12.615902 13.019048 14.00359
## 628 13.126832 13.644085 14.48284
## 630 13.131813 13.651292 14.49586
## 631 13.147928 13.738306 14.51532
## 633 13.164916 13.773073 14.56912
## 636 13.414307 13.930818 14.56396
## 640 12.857915 13.676599 14.56909
## 650 12.707269 13.395353 14.14077
## 652 12.610600 13.434527 14.26760
## 653 13.359460 13.808634 14.43135
## 655 13.058983 13.641802 14.54000
## 657 12.573004 13.172265 13.98698
## 662 12.775298 13.376557 14.19206
## 664 12.938270 13.471331 14.36254
## 669 12.727402 13.019270 13.53716
## 672 12.360333 12.935514 13.72048
## 674 10.667766 12.239504 13.04956
## 677 12.000031 12.431798 13.00205
## 680 11.256158 12.150173 13.05818
## 684 12.187641 12.602403 13.20961
## 686 12.278015 12.702713 13.17243
## 689 13.247943 13.785964 14.28051
## 690 13.135072 13.668010 14.24483
## 691 11.541530 12.178550 12.91978
## 692 12.218076 12.798730 13.40360
## 693 11.883676 12.648162 13.77107
## 694 11.980321 12.747428 13.89421
## 695 13.308986 13.923856 14.64770
## 698 12.320506 13.238914 14.19972
## 702 12.476076 13.080241 13.96114
## 703 13.141016 13.806508 14.72475
## 704 12.528068 12.991916 13.88838
## 705 12.503221 13.070368 13.98524
## 709 13.210357 13.907252 14.98647
## 710 12.657249 13.118962 13.77154
## 711 13.185547 13.685571 14.31310
## 714 13.235515 13.940207 14.96389
## 715 12.443541 13.306148 14.10947
## 718 13.728352 14.315974 15.07200
## 720 13.744232 14.330857 15.08222
## 722 12.467353 13.101863 13.92134
## 723 11.655058 12.675285 14.15549
## 725 12.777005 13.381695 14.14432
## 728 13.408270 14.064131 14.76618
## 730 13.353299 14.023197 14.76700
## 734 11.889455 12.419496 13.65293
## 735 11.971514 12.722425 13.78799
## 741 12.493704 13.213455 14.06187
## 749 11.120117 11.775255 12.59962
## 752 12.470680 12.894327 13.41007
## 753 12.533682 12.906918 13.55207
## 756 13.277667 13.795636 14.52256
## 759 12.494418 12.814561 13.17241
## 762 13.382559 14.158083 14.76013
## 765 12.496060 12.892563 13.47186
## 768 13.123732 13.580765 14.08156
## 769 12.978608 13.427862 14.04790
## 770 12.848019 13.647474 14.51263
## 771 12.381239 13.245854 14.02191
## 785 11.521304 12.566866 13.43779
## 797 12.736304 13.265014 13.86204
## 800 12.566171 12.960370 13.67187
## 805 13.041529 13.790476 14.65000
## 807 13.714771 14.382836 15.13984
## 809 11.610006 12.860137 13.95557
## 814 13.306938 14.227557 15.01697
## 815 12.871033 13.298857 13.99630
## 818 13.679190 14.255474 14.88337
## 820 13.573483 14.032905 14.55834
## 830 11.910312 12.672097 13.82077
## 837 12.986520 13.519000 13.93904
## 838 12.043696 12.482925 13.58034
## 843 12.613552 13.307428 13.89984
## 845 12.849188 13.205893 13.69659
## 851 12.362967 13.005690 13.70417
## 855 12.905547 13.411859 14.12447
## 856 12.922130 13.444615 14.17626
## 857 12.910432 13.426439 14.12068
## 859 12.204337 12.773611 13.60872
## 864 12.628501 13.280358 14.33197
## 870 12.647663 13.058806 13.51581
## 874 10.909851 11.478413 12.08479
## Log_NEI_PM25_Sum_10000m Log_NEI_PM25_Sum_15000m Log_NEI_PM25_Sum_25000m
## 3 6.573127301 6.581917457 6.875899578
## 5 3.772775514 3.785646441 4.160506364
## 6 0.909136688 3.174609213 3.190007668
## 7 7.987614168 8.438952213 8.651068360
## 8 5.412954990 6.643477321 7.806772886
## 9 0.002167007 0.002167007 6.362328519
## 14 0.000000000 4.008887068 6.425888035
## 16 6.048930476 6.233243618 6.430599790
## 18 3.704733653 3.990921996 7.215941444
## 20 5.514308113 5.514316235 5.581576769
## 24 3.736211829 3.736211829 5.889598800
## 26 0.095002496 0.459928419 4.954749959
## 27 3.806774950 3.806774950 4.009038112
## 28 1.679262359 4.157284985 4.262179249
## 33 5.600783800 5.600785697 5.600785697
## 37 3.469949250 3.469949250 4.025981256
## 42 4.459412758 4.459412758 4.464942181
## 44 0.415369656 4.921060227 7.007710124
## 46 2.979051282 3.682215047 4.798589937
## 47 3.934657901 3.954340731 5.591541212
## 48 0.749827071 0.749827071 1.910980767
## 50 3.499172352 3.499208906 3.524718296
## 52 5.499730906 5.604378483 5.651115063
## 56 1.368298538 1.433274393 3.986610956
## 62 0.154064439 0.922895296 6.059470875
## 65 4.280346515 4.964590561 5.126055482
## 68 5.387386184 5.557637424 5.614321909
## 69 5.383221344 5.387471994 5.614322970
## 72 4.195927963 4.389649474 4.482636658
## 73 0.877392590 0.877392590 2.421967650
## 78 4.885185476 5.768562126 6.031923352
## 80 3.855345818 3.855421829 4.193862181
## 81 3.767384675 4.661884865 5.236752554
## 82 4.457533974 6.742035970 7.011774601
## 89 6.611604485 6.991348429 7.452689651
## 90 3.565715550 4.719002427 6.563942254
## 92 2.495388607 3.176587483 4.001385870
## 93 3.159385007 3.550529268 5.140060167
## 94 1.818780506 2.266814933 2.550841174
## 96 4.254478808 4.647467621 6.120918757
## 99 3.706307682 3.710947514 3.712345740
## 109 0.333271620 0.790632469 3.812139538
## 110 4.545647131 4.855984697 5.942601711
## 111 5.120156544 6.845472149 7.157636535
## 114 5.516661282 5.742584323 6.248635449
## 115 5.752360839 5.936771607 6.259767190
## 117 6.701289647 6.788966506 6.902244553
## 119 5.154102699 6.171574614 7.151224200
## 129 4.694240615 5.405961012 5.995506916
## 130 0.830393044 2.115144738 4.394847811
## 134 2.843032172 3.481156246 4.102655981
## 137 2.702347687 3.235086472 4.133604173
## 139 0.769992891 1.251368594 3.772860085
## 140 1.712440732 3.368254820 4.625074703
## 144 4.824669308 5.072107311 6.891603751
## 148 6.357162282 7.385544009 7.626139935
## 151 0.006109714 0.407891699 2.945357611
## 153 4.764630929 4.899861348 5.910492678
## 154 4.131257887 4.470929755 5.168164254
## 158 5.130360868 5.145554399 5.247960712
## 167 3.484239567 3.493713139 4.061742582
## 172 0.054154153 1.376569712 4.654352001
## 173 4.532919982 4.575876892 4.658805066
## 175 6.163505035 6.281171646 6.675887099
## 177 6.406647329 7.202787120 7.815910531
## 180 4.886809658 5.004692084 5.179764784
## 185 1.478704684 2.010127692 2.164313007
## 193 6.266489943 6.990490938 7.477499801
## 196 3.783080227 3.786106642 5.141393336
## 197 5.249652307 5.946310992 6.671739304
## 200 3.060480638 4.779939380 6.919101312
## 202 7.501523603 7.501583897 8.282657208
## 209 0.006486985 1.716070459 4.754512453
## 213 5.370455363 5.384921118 5.384970969
## 217 0.599462851 1.594639095 5.582623395
## 219 1.269549668 5.946357992 6.415415464
## 220 1.350792727 2.065368875 5.474794266
## 230 5.514308113 5.514308113 5.581573488
## 232 5.304703651 5.514309762 5.581570310
## 233 0.000000000 0.092996168 7.603078995
## 236 4.518914831 5.429569704 5.665729872
## 239 1.883280924 1.996630283 4.942208432
## 240 1.884146867 1.884476465 4.934775958
## 242 0.088461947 0.088461947 0.091297713
## 244 0.218495146 0.218495146 0.219159678
## 245 0.084799427 0.084799427 0.084799427
## 246 4.366633590 5.398010459 5.406707081
## 247 3.529295654 3.664229181 4.051929792
## 253 6.856031200 7.479731872 9.090230180
## 257 6.851128031 7.019112747 7.889767904
## 261 3.681638328 5.496190002 6.870989934
## 264 3.442058504 3.660000510 5.225625872
## 266 2.974775196 5.868389000 6.090071611
## 268 2.815497291 3.496809099 4.819159200
## 273 5.704387676 7.298788327 7.639313627
## 282 0.417008840 1.246377304 6.029156485
## 283 5.767740081 5.852841217 5.944812262
## 287 4.433214773 4.434768897 4.998682066
## 288 4.369998030 4.559133339 5.077841582
## 290 4.675098070 4.776292672 6.149942505
## 300 8.118192372 9.229082590 9.606587474
## 301 8.822477885 9.328953737 9.337806116
## 304 0.853728117 0.874143422 4.867586034
## 311 5.635468925 5.645066500 6.392102966
## 312 5.641293898 5.656983290 6.203353802
## 314 6.855981385 6.855983961 6.856036509
## 315 3.759746945 6.454794951 8.900104574
## 316 3.759746945 5.017374758 8.900066773
## 317 3.759746945 3.946017104 8.900104574
## 320 5.026283660 5.146878815 5.163784964
## 322 6.599391378 6.603477759 6.614032933
## 323 0.000000000 0.017009384 1.788662155
## 325 5.599036896 5.601542276 5.676356023
## 333 6.288727476 7.318697281 7.346588731
## 336 5.629017966 6.438758810 6.810990858
## 337 0.169222158 0.169222158 0.170797435
## 339 0.002167007 0.013459924 4.811925195
## 342 0.013570838 0.559342631 5.416596712
## 351 7.287054486 7.733272622 8.001968871
## 355 0.000000000 3.744616784 4.879494283
## 356 5.541500054 5.547528738 6.632659668
## 359 3.556304798 3.596999050 5.309047192
## 360 4.641018615 4.779323170 4.921053997
## 366 4.873540691 6.218672523 9.024879677
## 368 4.635122014 5.771487937 5.943282488
## 369 8.225869079 8.256212350 8.331916362
## 370 2.428155209 3.243998189 5.367863220
## 373 0.887411801 0.888304570 7.654277584
## 374 7.605223858 8.304911992 8.307139220
## 376 7.447663061 8.564736581 8.797821373
## 377 8.022259746 8.122203154 8.931433156
## 385 7.123143907 7.205381810 7.571757279
## 386 0.631294397 4.966473470 5.419010641
## 391 1.876112364 1.943317165 5.121159995
## 392 6.941086722 8.313812627 8.326379376
## 393 0.837257138 3.791137359 6.311349925
## 396 3.434737551 4.423124070 5.182636365
## 401 3.118628526 5.103637950 8.318861576
## 406 5.185763314 5.230419823 5.475146838
## 407 4.144978914 5.302024558 6.359496317
## 411 5.116895283 5.396592986 5.698299312
## 412 5.116895283 5.396509207 5.697120862
## 415 6.122324592 6.217385290 6.498426027
## 416 6.145962179 6.248485003 6.483552531
## 422 8.414197109 8.417221452 8.500717094
## 423 2.130839348 3.891810879 4.457796185
## 425 0.062465092 0.062465092 0.271520144
## 435 0.347297617 0.347297617 0.442872553
## 436 7.261550380 7.519585574 7.899954666
## 437 6.567722655 6.778345412 6.951744675
## 438 2.877544333 4.685565040 7.337815320
## 443 7.176144908 7.201895723 7.743216123
## 446 3.556405834 4.489344246 5.397904517
## 447 7.167310521 7.202393768 7.746997997
## 451 0.000000000 0.000000000 2.055313219
## 452 5.151858147 6.136924240 7.430503866
## 453 5.605548591 6.624480342 7.642240423
## 458 4.274532392 4.494166109 4.613106053
## 464 5.269426689 5.751141154 6.317614460
## 467 5.162713314 5.238036233 6.024528867
## 472 3.533286635 3.719824390 4.656178229
## 473 5.940580914 5.940762170 5.947747807
## 477 5.150509752 5.160156738 5.160157533
## 483 5.086514979 5.311426636 7.001306655
## 490 6.856337049 6.951359849 7.695155915
## 492 6.850460792 6.948068000 7.716709222
## 493 3.923202408 3.970926066 3.971640813
## 494 5.302972750 5.306314284 5.488738612
## 496 3.556762044 3.748441702 5.490639529
## 503 3.633300816 3.633300816 3.633325118
## 506 0.054233174 0.054233174 1.329276955
## 507 4.869186392 5.140853868 5.140853868
## 509 5.463298438 7.335364339 7.346632501
## 514 3.326341691 3.326341691 3.358309785
## 515 3.780263466 3.780282600 4.446848952
## 516 5.168927425 5.695584774 5.904082148
## 517 1.661804612 1.661804612 3.299568830
## 524 6.162363706 6.249846981 6.297188412
## 525 7.958732595 7.958822153 7.967774004
## 527 2.256290693 3.195499898 5.118378279
## 528 2.183969443 3.064068964 5.119198586
## 532 7.755151494 8.102192048 8.192914028
## 533 6.750109173 7.042209251 7.586223900
## 535 6.300529849 8.075823517 8.326661480
## 536 6.938756597 7.886184995 8.093481132
## 549 4.201587027 6.761799672 7.034162709
## 550 3.592434729 4.182623377 5.638920549
## 553 3.698912191 5.193796372 5.900681879
## 555 0.025445761 0.116533104 0.171375323
## 556 0.215433908 0.372609282 3.900212848
## 558 1.782971837 2.107623905 6.749680772
## 559 0.259674551 0.695249451 0.695249451
## 566 4.181865969 4.551729690 4.781302348
## 568 0.001081405 0.001081405 0.169380012
## 575 3.816684934 4.272323085 4.319380742
## 579 6.800054866 7.006477001 8.329047369
## 581 1.352040095 1.352263173 3.620056584
## 583 2.822191345 3.952567193 6.717215816
## 589 4.113891183 4.869311518 4.938749519
## 603 1.437462648 6.303561819 6.306113866
## 604 4.346902641 5.048061501 8.109331963
## 610 2.739944479 2.739953420 3.073207311
## 611 2.726523269 2.726523269 2.766292452
## 614 0.099009674 0.099009674 4.315093510
## 616 4.697407129 4.869170514 5.052666449
## 617 0.824796643 0.825747073 0.860126401
## 619 0.000000000 0.000000000 0.970021054
## 621 5.994674963 6.014952199 6.015906665
## 622 6.667263184 6.732128773 6.732145235
## 625 4.731278126 5.348458155 7.510993585
## 626 2.543212454 2.597021636 4.907497617
## 628 7.072275617 7.277095744 7.321823846
## 630 7.072275617 7.281054973 7.321823846
## 631 7.058682831 7.158419640 7.322215757
## 633 7.054353137 7.155492024 7.321822222
## 636 5.889034298 6.233488023 6.322110628
## 640 5.748938979 6.093271798 6.460665105
## 650 7.082273846 7.301031298 7.469344146
## 652 7.506756049 7.522130353 8.047888974
## 653 5.720003664 6.953206631 7.633493436
## 655 5.025593921 6.101849438 7.071476549
## 657 4.447384983 5.457706746 6.254990913
## 662 6.466764187 6.554321077 6.567318872
## 664 4.923423244 4.960390939 5.417175345
## 669 6.812929047 6.813056372 6.813213123
## 672 0.148689587 0.148808918 4.527990467
## 674 4.202792510 4.202815804 4.203933740
## 677 1.018144947 1.121711365 1.167098952
## 680 0.195065515 4.163589109 5.307171179
## 684 4.960126676 6.951998208 6.996873823
## 686 6.903994515 6.980811678 6.996866983
## 689 5.683997424 6.157651365 6.790406547
## 690 5.969693661 6.334491981 6.789281996
## 691 1.599195712 3.436219368 3.436276852
## 692 1.200567011 2.034981991 2.035092367
## 693 1.495973287 1.530759645 6.075432621
## 694 0.060098156 2.637732114 4.464040361
## 695 5.896203752 7.131096365 8.084639096
## 698 6.870421838 6.957894036 7.151692612
## 702 7.273924935 7.322386440 7.409625056
## 703 4.637611862 7.577750416 7.709357547
## 704 3.706860830 4.024357993 7.333051286
## 705 2.221998824 4.363872627 4.460521217
## 709 7.494458855 7.558417411 8.119688597
## 710 4.447482092 4.453804172 4.492537183
## 711 5.601264792 5.612551010 5.694359333
## 714 3.731969416 4.345426275 7.241276512
## 715 5.662274882 6.940066534 7.111137936
## 718 6.752939248 7.451338130 7.735841753
## 720 6.950690801 7.015902661 7.569157262
## 722 3.215477251 3.222047007 5.806495193
## 723 2.460065920 4.416388599 9.537863652
## 725 4.656941061 5.298587899 8.735032841
## 728 3.365888617 4.631729575 5.744869246
## 730 3.385097313 4.648270257 5.784293200
## 734 0.071989909 0.134425380 2.213154926
## 735 0.652065686 3.428468129 4.822645078
## 741 1.185311751 4.208276619 6.794512509
## 749 0.002167007 0.002167007 0.002305168
## 752 0.004329329 0.004921094 0.682578339
## 753 0.004329329 0.680738736 0.683673941
## 756 5.614376929 5.642977775 5.683888895
## 759 4.441669770 4.705256348 6.632509938
## 762 5.385174166 5.733357474 6.253136433
## 765 6.659765658 6.677332216 6.769168366
## 768 5.776786893 5.782541315 5.951056182
## 769 5.147659810 5.782536805 5.832695502
## 770 4.685924602 5.227216281 7.769601877
## 771 7.895737741 8.258480284 8.634848794
## 785 0.383203882 0.383203882 0.674013335
## 797 0.000000000 2.609794114 4.405052221
## 800 3.841171059 3.841754695 3.841857004
## 805 6.635072818 6.725870939 7.271740342
## 807 2.801258311 4.946637691 5.365319687
## 809 2.502148747 3.447916511 5.840762459
## 814 3.515376495 3.580545308 4.021621037
## 815 3.056875628 3.067048704 5.017088309
## 818 5.799665004 5.920499965 6.171463785
## 820 5.068875914 5.369527344 6.315942422
## 830 5.724272838 5.736400517 6.270225059
## 837 4.635643881 4.755125788 8.250426011
## 838 4.485187652 4.485313086 7.705408358
## 843 7.952987548 8.015237332 8.406191761
## 845 5.191577890 5.276010226 5.283135099
## 851 1.923555192 2.765531758 3.045873032
## 855 5.171638561 5.265992529 5.607838153
## 856 5.107208394 5.228729189 5.574462547
## 857 4.953683985 5.256643524 5.622880402
## 859 1.768948308 5.115325846 5.242296030
## 864 3.562707181 3.811794671 5.444018358
## 870 6.764033982 6.764033982 6.789201836
## 874 0.002167007 0.106249031 0.106249031
## Log_NEI_PM10_Sum_10000m Log_NEI_PM10_Sum_15000m Log_NEI_PM10_Sum_25000m
## 3 6.691873127 6.701277408 7.14885842
## 5 4.437198843 4.462679325 4.67831103
## 6 0.928888905 3.674739043 3.74462870
## 7 8.209759579 8.648834554 8.85801911
## 8 5.771181130 6.868896765 8.02107151
## 9 0.016549626 0.016549626 6.89199158
## 14 0.000000000 4.016695044 6.96158011
## 16 6.230905819 6.427890313 6.64032578
## 18 3.939083577 4.229315143 7.33132719
## 20 5.712067870 5.712077632 5.78626709
## 24 3.879107227 3.879107227 6.61394661
## 26 0.623242822 1.721041539 5.47910147
## 27 3.881124278 3.881124278 4.07076879
## 28 1.761445434 4.277644933 4.76013767
## 33 6.374419098 6.374425609 6.37442561
## 37 4.251442675 4.251442675 5.10885255
## 42 5.171147507 5.171147507 5.19186458
## 44 1.601608644 4.986761370 7.32259063
## 46 3.124916053 3.786380064 4.91402286
## 47 3.972470041 3.991565279 6.72893640
## 48 1.344056790 1.344056790 2.18602982
## 50 3.640357549 3.640601688 3.67198158
## 52 6.352010627 6.445752853 6.46671681
## 56 1.766226226 1.821161371 4.39704822
## 62 0.824735519 1.406410010 6.30935018
## 65 4.920492658 5.424792063 5.57117902
## 68 5.539774020 5.723682180 5.79190017
## 69 5.534704807 5.539847835 5.79190107
## 72 4.837930403 5.077206511 5.23953068
## 73 2.134166441 2.134166441 2.92154216
## 78 5.037645284 5.840860360 6.14283048
## 80 4.261139465 4.261228315 4.71283417
## 81 4.594150316 5.212957258 5.73870926
## 82 4.764171414 7.225814169 7.45712516
## 89 6.789814991 7.153831447 7.60937827
## 90 4.505033869 5.292333685 8.20435889
## 92 2.919602647 3.734306469 4.49454263
## 93 4.674096097 4.796928979 5.57819605
## 94 2.361733302 3.505414896 3.63693979
## 96 4.558390170 4.970542976 6.36896504
## 99 3.852492982 3.877796344 3.88554757
## 109 1.396864549 1.666835564 4.93459512
## 110 5.214152768 5.542568741 6.43236149
## 111 5.759777383 7.391473191 7.75233355
## 114 5.739107291 6.046557139 6.57215607
## 115 5.935754141 6.096735355 6.51827279
## 117 6.853830317 6.949341443 7.07617188
## 119 5.371998228 6.340357776 7.31331027
## 129 5.299135402 5.780236279 6.36459696
## 130 0.925287536 2.708574385 4.90249846
## 134 3.279421510 4.481874064 4.97456371
## 137 3.199038472 3.891333189 4.98392388
## 139 0.780198898 1.690894353 4.03145006
## 140 2.742852849 3.689386058 5.08755692
## 144 5.708932959 5.964888754 7.57884867
## 148 7.088899534 7.999446793 8.25163433
## 151 0.046064300 1.017366969 3.90812481
## 153 5.388681313 5.634148405 6.62669935
## 154 5.048768615 5.471389204 6.24944277
## 158 5.279624730 5.292944255 5.38342347
## 167 3.580554576 3.592051955 4.19772925
## 172 0.142460316 1.724539588 4.78351854
## 173 4.630796678 4.706238327 4.78165654
## 175 6.169852302 6.301066753 6.73817278
## 177 6.623931681 7.317211079 7.95047924
## 180 4.989893264 5.102184716 5.35445199
## 185 2.499991644 2.850452375 3.01205478
## 193 6.559756016 7.276005069 7.88999414
## 196 4.270496133 4.278908627 5.49518811
## 197 5.350374602 6.076783738 6.85264357
## 200 3.526212765 4.957774810 7.06555311
## 202 7.560652272 7.561089361 8.32975951
## 209 0.048849201 2.537601139 4.90525151
## 213 5.945309458 5.954825317 5.95492074
## 217 1.989648685 2.347396195 6.35543187
## 219 1.305967140 5.962198100 6.80331599
## 220 2.750564493 2.984805476 6.24647394
## 230 5.712067870 5.712067870 5.78624652
## 232 5.336707155 5.712036775 5.78622660
## 233 0.000000000 0.559429628 8.35634292
## 236 5.003169190 6.037200743 6.22249610
## 239 2.645634032 3.003840496 5.54583175
## 240 2.648738397 2.649918170 5.51418855
## 242 0.537360001 0.537360001 0.55120772
## 244 1.057264055 1.057264055 1.05947188
## 245 0.519245192 0.519245192 0.51924519
## 246 5.443771772 6.007298509 6.02710022
## 247 4.147557065 4.537125918 4.92224375
## 253 7.300200294 7.908303166 9.39436185
## 257 7.299862691 7.459809894 8.26039330
## 261 4.097509081 5.787684128 7.40940208
## 264 3.921224494 4.174089564 5.61584826
## 266 3.257357701 7.028424403 7.14813557
## 268 3.527729758 4.044873892 5.18648720
## 273 6.024391729 7.419808056 7.88562398
## 282 0.512020568 2.106748175 6.22576485
## 283 6.139115867 6.261361328 6.39891241
## 287 4.587870339 4.598067931 5.09606948
## 288 4.530740713 4.696749800 5.16703624
## 290 5.056078758 5.138511805 6.40233066
## 300 8.207533143 9.443283303 9.87342277
## 301 9.089064457 9.535923297 9.54517885
## 304 1.378374573 1.468111215 5.01431307
## 311 5.884597645 5.901072044 6.60272315
## 312 5.894289266 5.915739615 6.45068394
## 314 7.449770938 7.449781881 7.44999891
## 315 4.578657995 6.767244512 9.30205927
## 316 4.578657995 5.544963527 9.30190997
## 317 4.578657995 4.729142504 9.30205927
## 320 5.145132681 5.253210355 5.27243084
## 322 7.022201333 7.025552024 7.03631759
## 323 0.000000000 0.123950847 1.80767387
## 325 5.934127410 5.942183655 6.00669714
## 333 6.564695761 7.702005122 7.73683060
## 336 5.977259942 6.762675294 7.21054837
## 337 0.193687675 0.193687675 0.20545219
## 339 0.016549626 0.099155260 5.04077619
## 342 0.099938181 1.929805121 6.20382297
## 351 7.472248201 7.879971317 8.14369128
## 355 0.000000000 4.066215064 5.35934221
## 356 5.926274373 5.940494604 7.19679491
## 359 3.980241651 4.007823896 5.72839804
## 360 4.989703590 5.141662095 5.31810134
## 366 5.147044079 6.471098509 9.11958164
## 368 4.940290723 5.984068026 6.23454544
## 369 8.330497300 8.371870190 8.49166110
## 370 2.885467124 3.576323296 5.84430246
## 373 0.887411801 0.894258814 7.77007078
## 374 7.678556514 8.574820437 8.57700982
## 376 7.530725589 8.616443178 9.01977623
## 377 8.106066380 8.204321019 9.09015313
## 385 7.246242299 7.375946441 7.72330715
## 386 1.937350668 5.185780270 5.63659203
## 391 2.576296850 2.616515325 5.73988574
## 392 7.394639022 8.628553369 8.63992977
## 393 1.001942147 4.078998639 6.36776727
## 396 3.516679695 4.499209123 5.40779760
## 401 3.189317151 5.720812312 8.62609359
## 406 5.530092936 5.587840005 5.85996520
## 407 4.386830925 5.475096040 6.55675958
## 411 5.492432777 5.815617032 6.17864668
## 412 5.492432777 5.815559547 6.17497310
## 415 6.311478418 6.404575989 6.75590036
## 416 6.327737833 6.430503191 6.71214423
## 422 9.004242475 9.008636736 9.08186171
## 423 3.922236288 4.560811963 4.93167695
## 425 0.402677894 0.402677894 0.67183416
## 435 1.685515723 1.685515723 1.86980759
## 436 7.873409829 8.030833139 8.37809467
## 437 6.990624209 7.135258073 7.29255223
## 438 3.049364875 4.856877838 7.56668617
## 443 7.427492638 7.453066113 8.05912632
## 446 3.880523771 4.961650663 6.00158185
## 447 7.418461991 7.451013248 8.07952968
## 451 0.000000000 0.000000000 2.21850382
## 452 5.982976957 6.607280837 7.75944843
## 453 6.224532751 7.070990108 7.97923012
## 458 5.086196330 5.270944212 5.32801676
## 464 5.995746553 6.453324181 6.83239499
## 467 5.764801290 5.811707439 6.54289053
## 472 3.737535022 4.009890540 5.21287875
## 473 6.804796803 6.807221456 6.81380293
## 477 5.212980975 5.223479137 5.22348488
## 483 5.154758502 5.462745147 7.38596162
## 490 7.458328990 7.521933650 8.06441449
## 492 7.454645982 7.519379552 8.07211393
## 493 4.536885474 4.695759743 4.69622453
## 494 5.611799396 5.620733765 5.85762835
## 496 4.079509100 4.382925114 5.86576717
## 503 4.233539431 4.233539431 4.23364200
## 506 0.356763813 0.356763813 1.45000483
## 507 6.430778668 6.495766163 6.49576616
## 509 5.804591551 7.724450993 7.73692356
## 514 5.141261662 5.141261662 5.14673384
## 515 4.155584466 4.155685590 4.88153108
## 516 5.434418923 6.072156328 6.41955201
## 517 2.151145234 2.151145234 4.34510958
## 524 6.454873305 6.531882187 6.57090365
## 525 7.990242971 7.990910317 8.00011841
## 527 2.499426615 3.674255034 5.30384393
## 528 2.239009535 3.364111690 5.30907425
## 532 7.784971551 8.143819121 8.24207217
## 533 6.847828235 7.142702736 7.73300509
## 535 6.471343096 8.111880421 8.38484447
## 536 7.167614420 8.003512856 8.23020862
## 549 4.518302789 7.071970083 7.38838411
## 550 4.402106697 4.919025199 7.07040587
## 553 4.052773804 5.335298668 6.03144795
## 555 0.180860540 0.899855637 1.06792747
## 556 0.215433908 0.842504552 4.08345303
## 558 2.309625996 2.617365637 6.79367211
## 559 0.482464414 1.953541976 1.95354198
## 566 4.496979836 4.981865970 5.22655192
## 568 0.008288545 0.008288545 0.88367312
## 575 4.268215695 4.666749178 4.71095271
## 579 6.870451457 7.077922536 8.38081533
## 581 1.840087319 1.841140195 4.06334930
## 583 2.893272451 4.271298144 6.82509922
## 589 4.236709862 5.323690921 5.38082825
## 603 1.437462648 6.441596119 6.44630903
## 604 4.513968317 5.218327830 8.26051230
## 610 3.028575262 3.028626798 3.44629662
## 611 3.015915488 3.015915488 3.18981564
## 614 0.288748803 0.288748803 4.32769471
## 616 5.434974856 5.607974507 5.72996904
## 617 1.577656291 1.581095609 1.63987967
## 619 0.000000000 0.000000000 0.97002105
## 621 6.428301982 6.448820516 6.45158853
## 622 7.288552890 7.423456551 7.42351998
## 625 5.103160210 5.580362810 7.58587125
## 626 2.564878088 2.912814377 5.37828917
## 628 7.244816515 7.443317320 7.49321485
## 630 7.244816515 7.447087728 7.49321485
## 631 7.233390219 7.318375742 7.49353588
## 633 7.229745306 7.315834334 7.49320432
## 636 6.052042439 6.427906284 6.52518238
## 640 5.927612937 6.284088173 6.72683518
## 650 7.293942245 7.477070844 7.66449451
## 652 7.795568806 7.818158791 8.42774779
## 653 5.927359690 7.064400645 7.91026223
## 655 5.163845047 6.303530939 7.42672732
## 657 4.598832748 5.711995736 6.58332048
## 662 6.542548788 6.723472781 6.73746218
## 664 5.076713547 5.155712632 5.65853835
## 669 7.232565783 7.233209378 7.23400126
## 672 0.637953516 0.638516154 5.04414649
## 674 4.967247511 4.967330933 4.97132875
## 677 1.116395778 1.682182265 1.86777206
## 680 0.250702483 4.242070851 5.51833281
## 684 5.152349471 7.173489418 7.21189121
## 686 7.119037686 7.196058940 7.21184877
## 689 5.777855316 6.414364061 6.95787374
## 690 6.125881666 6.464799044 6.96958788
## 691 1.970038674 3.675235645 3.67558377
## 692 1.221953184 2.533874604 2.53439005
## 693 2.928310206 2.991295083 6.23654052
## 694 0.072254631 2.676828991 5.09009725
## 695 6.119751482 7.275317867 8.26239635
## 698 7.008301282 7.089080169 7.29191938
## 702 7.368793520 7.571512222 7.67932315
## 703 4.884678468 7.619275647 7.77796153
## 704 3.859737364 4.212338433 7.63585683
## 705 3.100492160 4.711253069 4.94780129
## 709 7.636668031 7.696641947 8.26393259
## 710 4.708922040 4.727562972 4.78831215
## 711 5.625260279 5.637569749 5.79072413
## 714 4.022456275 4.571309622 7.43455534
## 715 6.090778103 7.278450682 7.43094883
## 718 6.848372063 7.572938006 7.89761042
## 720 7.051803514 7.117160331 7.71624966
## 722 3.473397713 3.487097458 6.19049898
## 723 2.462625745 4.596766883 9.67494332
## 725 5.219574715 5.795919844 8.80544703
## 728 3.855217496 4.814496255 6.02898038
## 730 3.876612913 4.860997005 6.12312852
## 734 0.453740219 0.745153121 2.86162509
## 735 1.128354547 3.808300211 5.06819518
## 741 1.199932685 4.585680895 6.86976320
## 749 0.016549626 0.016549626 0.01759675
## 752 0.032829816 0.034302869 1.41014974
## 753 0.032829816 1.404045931 1.41421495
## 756 6.148220808 6.208393683 6.24907426
## 759 4.537450161 4.779646113 7.16550840
## 762 5.410691695 5.827087806 6.46269820
## 765 6.775773299 6.795221305 6.98941572
## 768 5.850714734 5.903955271 6.16430773
## 769 5.289078694 5.903951178 5.96893388
## 770 4.837024579 5.507763222 7.91103169
## 771 7.983015452 8.331455366 8.73720942
## 785 1.521124306 1.521124306 1.64122484
## 797 0.000000000 2.961082274 5.62274142
## 800 3.957857514 3.961845766 3.96254349
## 805 7.383653953 7.432245070 7.83737200
## 807 2.829106087 5.037094544 5.47609007
## 809 3.620979540 4.324957151 6.71340199
## 814 4.269053107 4.446215260 4.69762958
## 815 3.342922750 3.400329803 5.09860317
## 818 6.398766582 6.472360901 6.71274581
## 820 5.096612058 5.398340732 6.33991384
## 830 6.588028561 6.605181633 6.98729714
## 837 4.900940822 4.994026168 8.32092847
## 838 4.943589332 4.944199269 8.14230668
## 843 8.058963040 8.147136594 8.58774234
## 845 6.663096277 6.704355118 6.70982594
## 851 3.398378647 3.788011919 4.54738075
## 855 5.992602346 6.096753644 6.63590621
## 856 5.936783850 6.061438334 6.59471725
## 857 5.874635444 6.123025958 6.66019714
## 859 2.422384406 5.864170528 6.01074009
## 864 4.296595399 4.714434224 6.38561606
## 870 6.914498701 6.914498701 6.95279250
## 874 0.016549626 0.621811988 0.62181199
## popdens_county popdens_zcta nohs somehs hs somecollege associate
## 3 35.4608142 5.408870e+02 7.300 15.800 30.60 20.9 7.600
## 5 75.3670384 1.301037e+02 4.300 13.300 27.80 29.2 10.100
## 6 67.6196640 1.857392e+02 5.800 11.600 29.80 21.4 7.900
## 7 228.7776327 3.345760e+02 7.100 17.100 37.20 23.5 7.300
## 8 228.7776327 9.708881e+01 2.700 6.600 30.70 25.7 8.000
## 9 56.8616114 9.593671e+00 11.100 11.600 46.00 17.2 4.100
## 14 228.7776327 5.995258e+01 9.700 21.600 39.30 21.6 5.200
## 16 129.6994555 4.962453e+02 3.000 17.100 39.00 22.4 6.600
## 18 112.9204615 1.693566e+02 8.800 19.300 36.00 20.2 4.600
## 20 338.8169052 7.494223e+02 8.600 24.600 27.70 19.4 4.300
## 24 32.7072704 8.061103e+01 5.700 14.300 37.60 21.3 7.300
## 26 8.2250282 5.351563e+02 32.700 11.800 45.00 7.9 2.600
## 27 2.7875053 3.622359e+03 1.700 0.000 22.30 36.4 14.700
## 28 160.1928537 2.578344e+03 23.900 17.400 27.30 20.8 4.300
## 33 5.8066084 2.076591e-01 8.600 27.000 35.20 21.0 4.900
## 37 27.0399026 1.860656e+02 3.200 6.500 32.10 30.3 10.200
## 42 5.1930520 1.233559e+01 5.400 13.100 41.70 21.3 5.900
## 44 32.2313135 8.428218e+01 3.600 12.200 28.70 28.2 5.400
## 46 54.7006637 1.102860e+02 6.500 11.400 31.20 21.9 7.800
## 47 10.9611305 1.348316e+01 9.800 15.300 44.40 17.4 4.900
## 48 12.0754263 2.392386e+01 7.300 16.800 29.80 22.1 14.600
## 50 29.3439194 8.871535e+02 6.200 9.800 33.10 20.6 6.000
## 52 194.5077727 4.439959e+02 4.500 9.400 23.60 20.2 4.900
## 56 83.2342134 3.114416e+02 17.700 16.200 31.30 16.9 4.000
## 62 17.2525020 1.862349e+01 2.100 6.400 35.00 21.0 15.800
## 65 60.2969592 2.449266e+03 19.900 16.700 26.50 23.0 6.900
## 68 14.5679417 1.313826e+03 3.400 9.000 28.70 27.6 8.900
## 69 14.5679417 1.313826e+03 4.000 8.600 27.30 25.8 9.800
## 72 16.1340771 1.681611e+02 17.400 14.700 20.40 23.8 7.900
## 73 0.7033431 1.989746e+01 25.000 0.000 0.00 0.0 0.000
## 78 39.8655559 2.477371e+03 18.200 17.900 27.20 23.1 6.100
## 80 19.8710752 5.191128e+01 3.800 8.200 30.00 27.1 9.100
## 81 934.2270959 1.361138e+02 13.700 7.900 25.60 21.4 8.100
## 82 934.2270959 3.289866e+03 13.700 6.500 21.60 25.1 6.100
## 89 934.2270959 4.759523e+03 6.700 4.500 18.60 22.3 8.800
## 90 934.2270959 8.553330e+02 8.900 11.700 27.90 28.5 9.200
## 92 51.0406477 2.503783e+02 8.900 10.000 23.50 26.7 9.500
## 93 48.8491845 1.756141e+03 20.400 10.700 25.10 20.7 8.500
## 94 39.8142883 1.514798e+02 2.200 7.100 23.30 28.9 10.400
## 96 1470.1549190 4.735472e+03 16.000 13.200 29.10 18.5 6.000
## 99 3.0257016 2.278377e+01 1.800 6.800 19.90 35.8 14.600
## 109 15.3664053 3.295319e+01 16.900 9.700 23.60 23.5 8.900
## 110 39.1784128 2.715956e+03 17.300 12.300 27.50 22.5 6.400
## 111 39.1784128 9.572534e+02 8.600 14.300 29.50 26.5 8.300
## 114 39.1784128 5.551599e+02 24.100 21.900 28.20 15.5 2.300
## 115 284.1008466 2.736183e+03 7.800 10.400 21.40 25.8 9.600
## 117 284.1008466 1.268332e+03 2.300 4.600 22.60 28.6 9.000
## 119 284.1008466 4.108063e+03 10.250 9.650 21.95 23.6 10.250
## 129 533.2112715 3.044469e+03 13.500 11.900 19.30 17.2 7.300
## 130 227.5673735 2.759938e+03 4.900 6.100 14.60 23.3 8.100
## 134 118.5559924 6.922987e+02 3.800 4.200 17.30 29.0 8.700
## 137 35.3894436 1.387108e+02 6.100 7.800 26.50 26.9 11.600
## 139 172.4698659 1.110865e+01 22.000 14.900 23.20 23.2 7.900
## 140 172.4698659 6.370455e+02 3.600 5.200 23.30 28.1 10.200
## 144 276.7216332 1.322282e+03 3.400 3.800 19.60 24.3 7.500
## 148 1514.5232760 4.017986e+02 23.700 22.000 23.20 14.6 4.400
## 151 4.8159332 9.296169e+00 0.900 5.800 20.70 26.2 5.600
## 153 44.5638347 9.089552e+02 1.700 5.000 14.90 21.7 7.700
## 154 17.0172758 1.140435e+03 5.000 10.100 26.90 24.1 10.300
## 158 566.4835822 3.737311e+03 12.900 14.400 31.60 17.6 6.000
## 167 550.8701088 2.080966e+03 6.700 1.900 7.10 4.9 1.800
## 172 106.9097716 6.691074e+01 3.000 11.500 42.00 21.6 6.900
## 173 106.9097716 1.696737e+02 0.000 0.000 0.00 100.0 0.000
## 175 487.7196735 5.686801e+02 1.600 5.000 30.00 20.4 8.000
## 177 487.7196735 1.518935e+03 7.400 15.100 34.90 20.4 5.500
## 180 3805.6112180 1.700077e+03 2.200 4.900 16.50 14.0 2.900
## 185 206.5630251 7.529816e+02 4.100 9.400 29.80 24.0 9.800
## 193 465.2033757 1.412277e+03 1.200 1.200 11.70 13.9 7.800
## 196 159.5048462 1.136995e+03 6.100 12.100 24.60 21.5 7.000
## 197 507.9153599 4.212013e+03 7.500 8.200 26.80 20.9 10.500
## 200 489.7519680 1.058394e+03 1.700 3.400 19.90 18.8 8.000
## 202 258.7654198 7.273668e+01 26.400 18.200 28.70 16.6 0.800
## 209 263.5621650 1.239154e+03 4.500 7.300 27.80 19.8 8.600
## 213 240.0509263 1.575058e+02 6.700 19.800 40.70 18.0 4.500
## 217 782.4150643 8.937393e+02 2.000 4.800 21.60 22.9 7.100
## 219 998.3536694 9.820147e+02 2.700 6.400 33.70 23.0 9.900
## 220 998.3536694 1.206792e+03 12.400 6.800 16.30 16.0 4.300
## 230 338.8169052 7.494223e+02 8.600 24.600 27.70 19.4 4.300
## 232 338.8169052 7.759940e+02 10.600 18.000 36.90 24.6 5.000
## 233 51.6015557 5.966352e+01 7.100 10.600 43.50 20.2 4.200
## 236 59.4714847 4.538844e+02 15.000 29.700 35.00 15.4 2.200
## 239 143.9259905 3.479869e+02 1.800 5.300 23.60 25.1 7.600
## 240 143.9259905 1.746682e+03 2.700 6.000 27.00 31.0 7.900
## 242 4.6161204 4.823496e+00 2.200 10.400 42.50 26.0 6.400
## 244 0.6714545 3.195858e+00 11.500 0.000 5.40 33.8 17.600
## 245 1.8742292 5.057176e+00 4.100 12.600 39.30 29.1 13.600
## 246 30.2953063 1.153763e+03 2.600 5.600 32.50 19.9 9.500
## 247 77.9288202 2.183669e+03 2.300 6.300 19.00 15.8 5.400
## 253 2121.6753070 3.152299e+03 8.400 9.100 29.60 24.9 8.300
## 257 2121.6753070 9.280679e+02 21.500 13.300 30.10 16.9 5.600
## 261 1080.9999200 1.256028e+03 1.400 1.900 9.90 14.8 5.300
## 264 382.5469159 1.186068e+03 9.600 10.000 27.60 23.1 7.300
## 266 612.1852832 5.548229e+02 1.400 5.500 32.20 30.4 9.400
## 268 197.6435285 7.115083e+02 1.500 3.600 19.10 22.6 7.400
## 273 145.2949266 3.004904e+02 3.600 8.300 31.10 27.8 8.000
## 282 64.7439800 2.171732e+01 3.400 6.100 41.30 31.6 6.100
## 283 222.0709998 1.649494e+03 8.400 17.000 40.60 19.2 6.800
## 287 37.8530224 6.552287e+01 5.600 8.000 37.70 16.6 8.700
## 288 37.8530224 6.552287e+01 5.600 8.000 37.70 16.6 8.700
## 290 164.6866215 7.286557e+02 6.600 16.800 36.20 20.3 6.000
## 300 383.8145872 6.830009e+02 6.100 12.900 41.40 25.6 5.200
## 301 383.8145872 1.415763e+03 6.500 11.800 40.70 21.0 7.100
## 304 112.4656913 1.165953e+03 8.300 15.400 46.40 16.2 6.600
## 311 225.1011703 1.727065e+03 3.200 8.900 35.30 22.8 5.500
## 312 225.1011703 1.727065e+03 2.100 3.900 32.20 20.7 9.000
## 314 133.4732566 1.110615e+03 4.300 14.100 37.50 19.4 6.600
## 315 297.1780980 3.057872e+02 1.300 23.100 35.10 17.4 0.000
## 316 297.1780980 8.576169e+02 5.200 14.200 40.40 19.2 7.100
## 317 297.1780980 1.649394e+03 5.600 10.700 33.40 25.2 7.100
## 320 89.4608365 8.185634e+01 5.200 8.000 35.70 22.9 10.000
## 322 27.2893762 9.857699e+01 3.200 3.100 39.30 22.5 12.900
## 323 11.8713070 6.448261e+00 1.500 5.500 53.60 5.8 24.500
## 325 26.7553698 9.917301e+01 5.200 10.000 41.30 21.3 8.000
## 333 37.8504274 7.818290e+02 4.200 6.700 38.60 30.1 11.800
## 336 139.2598121 4.388037e+02 9.900 12.900 36.10 26.7 7.700
## 337 6.0290014 6.738057e+00 4.600 6.600 36.10 24.9 10.300
## 339 8.8000506 9.915191e+00 0.000 0.000 64.30 21.4 14.300
## 342 6.2758149 7.722580e+00 3.600 2.100 45.50 36.1 9.400
## 351 119.6533117 7.502819e+02 5.700 9.500 33.70 24.5 7.600
## 355 12.8461480 1.492575e+01 6.700 10.200 37.00 23.0 7.500
## 356 81.4211806 3.135693e+02 4.600 8.100 37.20 21.4 8.000
## 359 91.5973753 8.679930e+01 6.200 8.300 32.90 20.8 4.300
## 360 65.3807673 1.173069e+02 4.700 6.900 32.70 22.5 8.900
## 366 384.8257603 1.400095e+03 6.000 12.800 26.10 21.2 4.800
## 368 73.2096452 7.892232e+01 7.600 7.600 27.50 19.7 7.300
## 369 15.6748796 2.150233e+01 7.200 19.700 44.10 22.1 4.800
## 370 31.9075499 4.475296e+01 11.900 12.300 35.50 16.9 4.600
## 373 69.9736832 1.722875e+01 8.700 19.200 39.00 18.1 6.000
## 374 69.9736832 8.072789e+02 9.100 14.200 36.10 20.3 5.000
## 376 373.2122172 3.085429e+02 9.200 22.500 30.10 19.6 2.800
## 377 20.8377484 5.327990e+01 3.600 13.100 42.20 16.7 4.300
## 385 36.7133483 8.322532e+02 4.400 13.200 43.10 23.5 4.200
## 386 59.0892400 2.252477e+02 0.000 16.300 54.70 9.5 3.600
## 391 519.5093907 3.954676e+02 1.800 2.500 18.00 16.8 6.100
## 392 519.5093907 1.102286e+03 5.800 7.900 32.10 20.1 7.100
## 393 112.7376864 1.815593e+02 0.000 16.500 64.80 18.7 0.000
## 396 690.6456182 3.270998e+03 7.700 14.400 32.60 22.7 6.900
## 401 2961.9891250 3.409478e+03 6.600 16.600 32.80 21.0 4.600
## 406 382.7440202 3.514398e+03 21.600 16.200 29.70 14.4 8.000
## 407 582.5352975 1.554721e+03 13.400 9.100 38.40 17.5 8.800
## 411 289.9737799 2.079717e+03 15.400 10.300 23.40 21.7 1.100
## 412 289.9737799 2.079717e+03 15.400 10.300 23.40 21.7 1.100
## 415 4793.7080190 1.320084e+04 1.400 0.800 9.00 8.5 1.800
## 416 4793.7080190 4.707373e+03 5.500 4.700 17.50 10.4 2.900
## 422 94.0773175 3.777545e+02 4.200 11.100 34.30 23.1 8.700
## 423 106.6422934 9.529861e+01 4.200 9.200 37.30 25.3 9.000
## 425 9.5434181 5.027406e+01 2.300 9.600 32.80 26.4 6.800
## 435 10.1522317 2.662313e+00 0.000 11.500 48.80 22.0 8.800
## 436 106.8370287 8.271917e+01 2.900 12.600 45.20 21.5 5.600
## 437 133.1651687 6.110224e+02 3.900 9.500 18.30 35.8 4.200
## 438 535.0400447 2.197567e+03 3.100 8.200 21.50 27.9 9.900
## 443 1148.4304360 1.843374e+03 27.400 25.400 29.00 11.8 2.800
## 446 1148.4304360 9.937758e+02 1.300 4.100 17.20 17.6 6.400
## 447 1148.4304360 1.002475e+03 16.900 12.500 19.70 14.3 5.600
## 451 5.4561216 3.960187e+00 2.400 13.800 29.70 38.6 6.900
## 452 273.7293946 1.127257e+03 1.100 3.100 19.20 22.0 10.100
## 453 803.7592150 1.957523e+03 6.800 7.400 23.30 22.6 7.600
## 458 85.2446210 2.090478e+02 3.900 5.900 31.30 21.9 12.400
## 464 12.3743778 1.102200e+03 2.700 7.000 30.70 28.4 9.200
## 467 239.2640025 6.489028e+02 1.200 5.200 40.30 23.9 10.200
## 472 20.0374771 2.966812e+01 8.300 14.300 31.00 22.3 6.300
## 473 125.8578542 7.766601e+02 6.600 8.100 22.90 25.8 8.100
## 477 44.0413044 7.526151e+01 6.300 17.700 32.60 24.4 8.500
## 483 215.6845729 1.622551e+02 2.800 4.800 28.00 23.5 7.700
## 490 1991.3164500 1.915718e+03 7.800 19.900 31.10 22.9 4.400
## 492 1991.3164500 5.992744e+02 2.200 11.700 18.80 16.7 14.100
## 493 11.6377665 1.711518e+03 3.500 8.800 30.30 27.4 7.600
## 494 6.9005193 3.716512e+01 2.200 6.300 37.90 22.1 8.400
## 496 6.9005193 4.243751e+01 2.100 6.600 32.90 24.6 8.900
## 503 16.2721512 4.382716e+00 3.600 6.100 28.30 22.2 13.000
## 506 1.5962854 1.574446e+00 4.600 8.400 43.00 22.6 6.000
## 507 18.3787182 1.852130e+01 3.300 6.400 37.30 23.9 6.900
## 509 607.8688394 2.446117e+03 12.000 11.100 27.30 23.2 4.300
## 514 19.3050871 6.077509e+01 4.300 11.800 30.40 22.8 9.600
## 515 20.0339410 3.576645e+01 1.300 5.100 32.30 24.0 7.900
## 516 95.4692804 3.322028e+03 21.900 20.500 30.90 16.7 3.400
## 517 95.4692804 7.582569e+00 0.000 21.300 32.00 42.2 4.400
## 524 60.5307259 1.024548e+02 2.400 7.400 35.40 25.5 8.800
## 525 76.8261839 2.655947e+02 0.400 0.500 12.50 6.2 7.500
## 527 190.7560141 2.606413e+02 4.100 6.400 34.10 20.9 7.200
## 528 190.7560141 1.434396e+03 11.500 17.600 34.40 15.1 5.800
## 532 1499.8024730 8.333857e+02 8.100 9.900 46.60 13.5 2.500
## 533 896.3284316 2.971920e+03 13.200 24.000 37.90 16.2 2.400
## 535 2398.2785010 6.867870e+03 7.300 17.400 39.10 20.2 5.700
## 536 345.6733525 2.551158e+02 2.300 5.700 49.10 16.9 6.300
## 549 117.5795993 2.677302e+02 4.400 11.800 42.90 16.0 6.400
## 550 220.3749435 1.543258e+03 2.800 3.700 24.50 25.6 8.800
## 553 21.2173544 4.259276e+02 6.500 6.200 12.90 22.7 4.600
## 555 2.8764507 4.425293e+01 5.500 6.900 26.30 27.5 4.400
## 556 5.6915564 4.285971e+01 8.600 8.100 30.80 21.7 14.400
## 558 9.1075031 1.370960e+02 6.300 12.700 30.90 24.4 9.300
## 559 29.1525842 1.729465e+02 5.100 5.900 19.60 19.6 5.700
## 566 340.3141664 1.653110e+03 3.800 15.600 39.30 20.5 10.700
## 568 8.4720787 7.332968e+00 0.400 5.500 32.70 21.6 9.000
## 575 231.6574260 9.287181e+02 1.300 3.300 21.20 17.6 6.600
## 579 3100.5067700 2.407329e+03 3.700 6.900 33.60 18.5 9.200
## 581 27.4855283 1.945556e+01 6.900 9.900 38.30 19.7 14.200
## 583 851.2357955 1.782710e+03 0.600 1.900 8.10 7.8 2.700
## 589 189.0697387 9.136056e+02 3.500 6.500 29.00 28.3 11.700
## 603 20.5140465 1.191247e+01 7.800 9.400 40.50 25.4 8.800
## 604 677.8190839 1.434718e+03 10.600 11.100 22.10 20.4 5.700
## 610 99.5779870 3.575044e+02 8.400 12.400 28.90 23.0 7.600
## 611 99.5779870 1.154858e+02 8.400 12.400 28.90 23.0 7.600
## 614 10.2237354 2.457600e+01 4.900 10.100 29.70 20.4 13.700
## 616 416.5081331 6.727994e+02 5.100 5.500 14.30 17.2 5.900
## 617 63.0982122 1.289339e+02 4.200 5.400 16.40 20.7 8.300
## 619 0.2631477 9.188156e-01 7.000 6.900 39.60 17.8 10.500
## 621 32.7658189 3.688392e+02 2.500 3.600 21.80 23.9 9.100
## 622 3.1185601 5.339576e+00 11.800 3.600 26.00 16.2 20.500
## 625 304.3230514 7.721269e+02 3.500 7.700 33.40 23.2 8.100
## 626 134.3756043 7.347544e+02 5.200 18.400 33.70 23.8 8.500
## 628 1081.0755160 1.937322e+03 7.600 17.400 21.60 17.3 5.800
## 630 1081.0755160 1.937322e+03 7.600 17.400 21.60 17.3 5.800
## 631 1081.0755160 1.138168e+03 9.300 22.700 35.60 18.5 5.000
## 633 1081.0755160 1.766753e+03 9.300 22.700 35.60 18.5 5.000
## 636 844.0560682 7.475024e+02 3.700 11.600 28.70 19.1 3.500
## 640 763.2187561 1.225476e+03 10.900 16.700 33.10 21.5 5.900
## 650 236.9250210 3.887089e+02 1.800 7.900 41.50 25.4 9.500
## 652 500.4640531 1.553084e+03 7.000 18.900 41.50 21.1 6.100
## 653 500.4640531 1.252522e+03 2.500 5.700 29.80 23.4 9.400
## 655 224.0157960 8.769978e+02 12.100 16.000 34.30 18.5 8.400
## 657 447.6704633 1.049876e+03 6.000 17.400 32.50 21.1 6.300
## 662 252.0806270 7.569190e+02 9.700 17.400 29.70 37.4 0.600
## 664 506.8050307 7.892833e+02 2.400 11.900 20.70 25.9 10.800
## 669 33.8199501 5.575203e+01 5.100 14.500 33.10 23.6 6.700
## 672 26.1173068 1.861925e+00 12.600 24.500 46.40 9.3 4.600
## 674 24.3100485 1.371803e+01 9.100 17.600 37.30 21.3 2.100
## 677 20.1780332 3.355024e+01 2.000 3.000 21.10 28.7 11.300
## 680 28.1864282 4.875937e+01 11.400 12.400 33.40 26.4 7.100
## 684 29.8252511 1.285163e+03 3.300 5.800 24.80 32.9 9.100
## 686 29.8252511 1.141449e+03 3.900 11.300 28.80 29.6 8.300
## 689 658.2795860 2.815162e+03 4.600 7.600 23.60 25.7 7.800
## 690 658.2795860 9.392533e+02 3.700 6.000 19.80 20.2 5.100
## 691 9.1123692 1.691982e+01 3.400 8.400 27.30 27.5 12.600
## 692 4.8813373 2.262715e+01 3.000 7.600 32.40 24.6 8.600
## 693 282.3994922 4.333113e+02 4.400 5.500 19.20 22.7 9.000
## 694 75.4885553 3.054594e+01 8.600 9.900 44.20 12.5 6.400
## 695 646.9713784 3.889874e+03 2.900 8.400 33.40 20.6 6.400
## 698 646.9713784 3.613702e+02 5.300 4.700 46.80 13.3 14.000
## 702 185.4727921 4.376480e+02 7.300 10.700 42.10 14.7 8.100
## 703 399.4821765 1.196322e+03 3.100 13.500 49.70 20.3 3.300
## 704 80.5908680 5.302185e+02 9.200 11.200 51.10 8.7 8.000
## 705 53.5676469 3.772888e+02 0.000 0.000 0.00 79.4 0.000
## 709 1173.9499840 2.370235e+03 7.600 15.800 45.30 16.7 5.700
## 710 135.5522610 5.863915e+02 10.100 18.500 44.60 10.6 5.700
## 711 180.3497596 8.184597e+02 3.400 6.800 43.30 18.4 11.500
## 714 639.3534244 2.672717e+03 1.600 5.000 23.10 16.1 6.400
## 715 310.9685288 5.707147e+02 4.100 6.600 27.00 18.2 10.200
## 718 4393.6457430 9.938713e+03 4.550 11.075 45.00 16.3 5.950
## 720 4393.6457430 4.098322e+03 4.400 13.200 22.40 15.6 4.900
## 722 93.6298585 1.562325e+02 4.000 8.700 42.80 15.8 8.000
## 723 93.6298585 3.101584e+01 3.600 10.800 47.60 16.7 7.200
## 725 185.7410663 7.372225e+02 10.800 18.800 47.00 9.8 4.900
## 728 590.8582870 3.232986e+03 14.700 14.400 30.30 17.3 5.700
## 730 590.8582870 1.229869e+03 3.800 7.400 28.10 19.6 5.400
## 734 20.8210161 2.328907e+01 9.300 11.800 37.30 19.2 7.700
## 735 66.0674464 3.107408e+02 4.700 8.300 28.60 19.8 8.300
## 741 196.0957023 0.000000e+00 39.400 0.000 21.20 18.2 0.000
## 749 0.6278594 4.139258e-01 0.000 33.000 30.40 25.4 1.300
## 752 14.0376351 4.360830e+01 3.500 7.500 32.00 26.5 8.600
## 753 14.0376351 2.369572e+02 3.500 7.500 32.00 26.5 8.600
## 756 239.4941639 1.310463e+03 5.400 14.000 23.00 20.1 1.300
## 759 40.3834167 1.472121e+02 8.000 9.100 34.30 32.0 2.900
## 762 1049.4266430 2.514901e+03 10.275 12.825 30.05 25.3 7.775
## 765 58.9806697 1.155230e+03 17.000 15.000 28.30 22.5 5.700
## 768 305.2569002 1.550880e+03 48.000 18.900 18.10 10.9 1.800
## 769 305.2569002 1.250256e+03 48.000 18.900 18.10 10.9 1.800
## 770 927.5773904 1.054116e+03 30.600 19.900 28.30 16.1 1.700
## 771 927.5773904 5.884356e+02 15.000 14.900 29.30 23.2 7.400
## 785 37.3422486 8.839382e+01 4.900 6.300 18.90 26.7 6.900
## 797 154.9786308 6.283662e+02 0.900 2.700 23.40 26.6 9.700
## 800 112.6440858 1.837190e+03 4.000 6.100 18.90 17.2 6.400
## 805 288.4485648 8.413824e+02 8.500 14.900 31.00 23.4 5.600
## 807 1068.2611400 3.864172e+03 12.700 6.600 16.50 12.8 4.800
## 809 73.1173460 5.135142e+01 5.200 8.300 36.00 15.9 11.700
## 814 34.7020380 1.121811e+02 11.100 12.100 29.00 16.5 4.400
## 815 529.1733396 6.378911e+02 7.800 12.800 28.60 22.3 7.800
## 818 1732.1877740 2.413394e+03 0.000 0.000 44.70 37.5 17.800
## 820 880.2351104 5.488993e+02 10.700 14.100 31.50 19.8 2.800
## 830 125.2400856 9.627495e+02 4.400 11.500 42.70 18.1 4.700
## 837 82.6786703 7.796840e+02 0.800 4.100 25.40 24.6 5.700
## 838 70.5559328 8.021284e+02 3.700 9.200 37.20 20.3 5.800
## 843 91.6669646 3.804301e+02 4.800 4.900 34.40 22.5 7.800
## 845 180.7712563 1.237443e+03 10.600 9.600 34.60 21.9 7.400
## 851 97.9927046 2.587213e+02 2.300 6.400 37.90 23.5 10.900
## 855 1515.8208580 4.980709e+03 0.000 3.900 11.20 10.7 3.900
## 856 1515.8208580 1.580861e+03 4.700 8.000 35.60 23.9 7.700
## 857 1515.8208580 8.107966e+02 0.000 3.900 11.20 10.7 3.900
## 859 143.1170929 3.639882e+01 3.700 3.700 28.00 23.7 12.500
## 864 273.9173936 1.031774e+03 3.100 6.400 25.40 23.8 9.100
## 870 13.1874175 2.323895e+02 0.000 0.000 71.90 28.1 0.000
## 874 0.8096509 4.475036e+00 0.600 4.500 25.80 29.7 11.100
## bachelor grad pov hs_orless urc2013 urc2006 aod
## 3 12.700 5.100 19.00000 53.700 4 4 36.000000
## 5 10.000 5.400 8.80000 45.400 4 4 43.416667
## 6 13.700 9.800 15.60000 47.200 4 4 33.000000
## 7 5.900 2.000 25.50000 61.400 1 1 39.583333
## 8 17.600 8.700 7.30000 40.000 1 1 38.750000
## 9 7.100 2.900 8.10000 68.700 1 1 40.363636
## 14 2.200 0.400 13.30000 70.600 2 2 42.636364
## 16 7.800 4.200 23.20000 59.100 3 3 42.000000
## 18 7.600 3.500 23.00000 64.100 3 3 39.500000
## 20 9.500 5.900 37.70000 60.900 3 3 31.250000
## 24 10.300 3.500 19.30000 57.600 2 2 35.833333
## 26 0.000 0.000 15.60000 89.500 4 5 12.833333
## 27 13.600 11.400 64.30000 24.000 4 4 29.250000
## 28 4.200 2.000 29.50000 68.600 1 1 46.333333
## 33 2.700 0.600 35.90000 70.800 4 5 8.833333
## 37 12.300 5.400 7.10000 41.800 2 2 29.000000
## 42 8.100 4.500 15.30000 60.200 6 6 63.125000
## 44 16.300 5.700 9.90000 44.500 2 2 43.300000
## 46 14.000 7.300 16.60000 49.100 4 4 33.333333
## 47 5.400 2.800 23.40000 69.500 6 6 51.000000
## 48 5.500 3.900 32.90000 53.900 5 5 37.571429
## 50 13.900 10.300 23.60000 49.100 5 5 32.166667
## 52 21.700 15.600 23.70000 37.500 3 3 49.125000
## 56 9.300 4.600 21.00000 65.200 3 3 36.166667
## 62 11.100 8.700 10.10000 43.500 6 6 32.363636
## 65 5.400 1.500 31.30000 63.100 3 3 83.363636
## 68 14.900 7.400 10.90000 41.100 5 5 17.000000
## 69 16.900 7.700 12.60000 39.900 5 5 39.571429
## 72 10.800 5.100 22.30000 52.500 4 4 53.111111
## 73 0.000 75.000 0.00000 25.000 6 5 5.000000
## 78 4.900 2.500 22.10000 63.300 3 3 55.875000
## 80 15.400 6.300 9.80000 42.000 5 5 26.833333
## 81 15.400 7.800 5.70000 47.200 1 1 32.909091
## 82 20.100 6.900 10.30000 41.800 1 1 55.090909
## 89 25.300 13.800 6.90000 29.800 1 1 96.600000
## 90 10.000 3.800 21.80000 48.500 1 1 24.083333
## 92 13.300 8.100 14.40000 42.400 3 4 45.500000
## 93 11.000 3.600 12.70000 56.200 3 3 38.250000
## 94 19.000 9.200 11.30000 32.600 5 5 37.181818
## 96 12.600 4.500 13.30000 58.300 1 1 82.333333
## 99 13.900 7.300 14.80000 28.500 6 6 45.166667
## 109 13.200 4.200 9.40000 50.200 2 2 21.800000
## 110 9.700 4.300 12.20000 57.100 2 2 50.272727
## 111 8.700 4.200 20.90000 52.400 2 2 26.583333
## 114 5.900 2.100 41.60000 74.200 2 2 27.181818
## 115 16.600 8.400 10.00000 39.600 1 1 77.000000
## 117 21.800 11.100 6.80000 29.500 1 1 53.000000
## 119 16.350 7.975 12.12500 41.850 1 1 74.200000
## 129 19.500 11.300 15.90000 44.700 1 1 86.375000
## 130 27.600 15.400 5.40000 25.600 3 3 38.909091
## 134 22.700 14.300 5.80000 25.300 3 3 44.181818
## 137 14.300 6.900 11.30000 40.400 3 3 60.300000
## 139 5.600 3.200 17.00000 60.100 3 3 15.363636
## 140 21.600 8.000 4.40000 32.100 3 3 26.250000
## 144 27.500 13.900 8.20000 26.800 2 2 59.666667
## 148 9.300 2.900 32.00000 68.900 1 1 53.100000
## 151 25.700 15.200 4.20000 27.400 2 2 26.333333
## 153 29.700 19.300 9.50000 21.600 3 3 29.333333
## 154 15.400 8.200 14.40000 42.000 4 4 37.333333
## 158 10.900 6.500 22.10000 58.900 3 3 53.400000
## 167 26.800 50.700 6.30000 15.700 3 3 37.000000
## 172 9.500 5.600 11.80000 56.500 4 4 67.000000
## 173 0.000 0.000 0.82500 0.000 4 4 64.090909
## 175 22.400 12.700 3.50000 36.600 2 2 52.000000
## 177 10.200 6.400 31.80000 57.400 2 2 47.500000
## 180 26.800 32.700 18.90000 23.600 1 1 59.500000
## 185 14.300 8.600 12.30000 43.300 3 3 31.000000
## 193 37.600 26.500 1.10000 14.100 1 1 22.000000
## 196 20.400 8.400 38.90000 42.800 3 3 46.083333
## 197 18.400 7.700 11.40000 42.500 1 1 55.000000
## 200 30.600 17.500 5.50000 25.000 1 1 43.695334
## 202 7.600 1.700 42.30000 73.300 2 2 41.500000
## 209 19.600 12.300 12.50000 39.600 3 3 43.695334
## 213 7.700 2.700 24.90000 67.200 3 3 48.714286
## 217 29.300 12.300 6.40000 28.400 2 2 42.750000
## 219 15.400 8.800 15.00000 42.800 2 2 49.833333
## 220 27.900 16.200 11.00000 35.500 2 2 48.666667
## 230 9.500 5.900 37.70000 60.900 3 3 33.000000
## 232 3.300 1.600 37.80000 65.500 3 3 33.000000
## 233 9.200 5.200 14.50000 61.200 5 5 36.500000
## 236 2.500 0.100 48.30000 79.700 3 3 28.571429
## 239 27.500 9.000 6.60000 30.700 3 3 49.555556
## 240 16.500 8.900 13.10000 35.700 3 3 40.250000
## 242 8.600 3.900 10.00000 55.100 6 6 27.600000
## 244 24.500 7.200 12.50000 16.900 6 6 23.500000
## 245 1.200 0.000 25.80000 56.000 6 6 27.200000
## 246 19.700 10.100 4.50000 40.700 5 5 41.500000
## 247 23.800 27.400 20.70000 27.600 4 4 38.700000
## 253 13.600 6.000 9.20000 47.100 1 1 103.000000
## 257 8.100 4.500 16.20000 64.900 1 1 101.500000
## 261 36.300 30.300 2.20000 13.200 2 2 62.777778
## 264 14.900 7.500 9.40000 47.200 2 2 51.888889
## 266 14.600 6.600 2.50000 39.100 2 2 60.000000
## 268 30.800 15.100 2.90000 24.200 2 2 54.500000
## 273 13.800 7.400 13.30000 43.000 2 2 39.000000
## 282 6.600 4.900 12.60000 50.800 2 2 34.818182
## 283 4.700 3.300 32.40000 66.000 3 3 45.333333
## 287 15.700 7.600 5.60000 51.300 5 5 40.250000
## 288 15.700 7.600 5.60000 51.300 5 5 40.833333
## 290 9.800 4.400 18.80000 59.600 4 4 46.375000
## 300 6.100 2.800 40.60000 60.400 2 2 46.000000
## 301 9.000 3.900 11.80000 59.000 2 2 67.000000
## 304 5.400 1.700 33.00000 70.100 2 4 48.181818
## 311 13.000 11.300 16.50000 47.400 3 3 41.666667
## 312 18.300 13.700 6.20000 38.200 3 3 41.666667
## 314 11.800 6.200 20.30000 55.900 4 4 36.090909
## 315 9.000 14.000 0.00000 59.500 3 3 43.916667
## 316 9.500 4.400 14.10000 59.800 3 3 45.909091
## 317 12.600 5.400 15.00000 49.700 3 3 43.916667
## 320 14.300 3.800 11.00000 48.900 4 4 34.111111
## 322 12.400 6.600 3.10000 45.600 5 5 46.666667
## 323 8.500 0.600 0.00000 60.600 6 6 39.125000
## 325 9.600 4.600 16.90000 56.500 5 5 33.700000
## 333 6.500 2.100 9.80000 49.500 3 3 38.600000
## 336 4.900 1.900 17.70000 58.900 3 3 42.000000
## 337 12.600 4.800 10.40000 47.300 6 6 36.181818
## 339 0.000 0.000 0.00000 64.300 6 6 20.500000
## 342 1.300 2.100 13.70000 51.200 2 2 28.750000
## 351 9.100 10.000 15.60000 48.900 3 3 31.272727
## 355 9.300 6.200 17.40000 53.900 3 4 31.666667
## 356 12.400 8.300 11.80000 49.900 4 4 48.272727
## 359 16.900 10.600 12.00000 47.400 5 5 29.916667
## 360 13.700 10.600 10.30000 44.300 4 4 32.750000
## 366 19.500 9.700 21.70000 44.900 2 2 66.900000
## 368 16.600 13.800 13.10000 42.700 5 5 34.333333
## 369 2.100 0.000 19.50000 71.000 6 6 30.583333
## 370 9.000 9.800 18.50000 59.700 6 6 32.090909
## 373 6.600 2.300 15.20000 66.900 4 4 35.500000
## 374 11.100 4.100 21.80000 59.400 4 4 40.181818
## 376 10.400 5.400 33.20000 61.800 3 3 40.000000
## 377 12.200 7.900 11.10000 58.900 3 3 40.000000
## 385 8.800 2.800 14.10000 60.700 2 2 43.695334
## 386 14.100 1.800 19.20000 71.000 4 5 32.916667
## 391 29.000 25.800 1.50000 22.300 2 2 44.000000
## 392 18.300 8.600 5.30000 45.800 2 2 45.416667
## 393 0.000 0.000 0.00000 81.300 2 2 49.333333
## 396 9.700 6.000 12.00000 54.700 2 2 58.333333
## 401 11.100 7.300 21.20000 56.000 1 1 53.416667
## 406 7.000 3.100 21.40000 67.500 2 2 29.600000
## 407 8.700 4.000 17.40000 60.900 2 2 36.714286
## 411 15.600 12.600 28.60000 49.100 3 3 29.625000
## 412 15.600 12.600 28.60000 49.100 3 3 29.625000
## 415 36.400 42.100 6.40000 11.200 1 1 52.000000
## 416 35.100 24.100 18.20000 27.700 1 1 52.000000
## 422 12.100 6.500 12.60000 49.600 4 4 56.800000
## 423 10.300 4.800 10.10000 50.700 4 4 42.750000
## 425 13.900 8.200 11.00000 44.700 5 5 38.000000
## 435 5.700 3.100 11.50000 60.300 5 5 27.500000
## 436 8.700 3.400 7.20000 60.700 4 4 34.500000
## 437 21.600 6.700 28.20000 31.700 4 4 26.250000
## 438 18.600 10.800 15.10000 32.800 2 2 74.428571
## 443 3.000 0.700 34.30000 81.800 1 1 95.000000
## 446 30.600 22.900 2.90000 22.600 2 2 116.000000
## 447 15.300 15.700 25.40000 49.100 1 1 95.000000
## 451 6.500 2.000 10.10000 45.900 5 5 40.000000
## 452 30.900 13.600 3.60000 23.400 2 2 67.400000
## 453 22.500 9.800 8.50000 37.500 1 1 43.695334
## 458 16.200 8.400 7.80000 41.100 4 4 35.444444
## 464 15.200 6.700 26.90000 40.400 3 3 29.666667
## 467 12.000 7.300 4.30000 46.700 2 2 24.000000
## 472 12.900 4.900 19.00000 53.600 5 5 33.666667
## 473 19.100 9.400 11.50000 37.600 3 3 38.090909
## 477 6.800 3.700 27.30000 56.600 5 5 32.666667
## 483 22.200 11.000 6.30000 35.600 2 2 36.272727
## 490 9.000 4.800 29.10000 58.800 1 1 73.833333
## 492 11.800 24.800 0.00000 32.700 1 1 73.833333
## 493 16.400 6.000 13.40000 42.600 4 4 28.200000
## 494 17.100 6.100 8.90000 46.400 5 5 29.000000
## 496 17.500 7.400 9.30000 41.600 5 5 29.333333
## 503 13.300 13.500 11.90000 38.000 4 4 24.200000
## 506 12.200 3.300 12.40000 56.000 6 6 22.500000
## 507 15.100 7.300 11.40000 47.000 5 5 22.833333
## 509 14.700 7.400 19.80000 50.400 3 3 42.000000
## 514 15.300 5.800 12.30000 46.500 5 5 27.777778
## 515 22.900 6.500 2.70000 38.700 3 3 31.600000
## 516 5.000 1.700 27.70000 73.300 1 1 20.500000
## 517 0.000 0.000 5.15000 53.300 1 1 15.416667
## 524 12.900 7.600 5.40000 45.200 5 5 30.666667
## 525 35.300 37.600 4.40000 13.400 2 2 58.166667
## 527 19.500 7.700 4.50000 44.600 3 3 92.500000
## 528 11.100 4.600 28.10000 63.500 3 3 95.800000
## 532 16.500 2.900 3.20000 64.600 2 2 70.888889
## 533 5.400 0.900 41.30000 75.100 2 2 75.375000
## 535 8.300 1.900 32.60000 63.800 1 1 63.111111
## 536 14.900 4.800 8.80000 57.100 2 2 58.000000
## 549 12.700 5.700 10.20000 59.100 3 3 46.444444
## 550 19.700 14.800 8.90000 31.000 3 3 12.090909
## 553 26.700 20.400 6.80000 25.600 3 3 11.909091
## 555 23.300 6.100 10.20000 38.700 5 5 23.083333
## 556 14.200 2.200 6.60000 47.500 5 5 15.416667
## 558 10.000 6.400 14.40000 49.900 4 4 7.800000
## 559 22.700 21.400 9.60000 30.600 4 4 19.444444
## 566 6.700 3.400 16.10000 58.700 1 1 71.400000
## 568 17.600 13.100 10.60000 38.600 6 6 25.166667
## 575 21.800 28.100 7.90000 25.800 3 3 48.142857
## 579 17.400 10.700 5.50000 44.200 1 1 43.695334
## 581 6.100 4.900 10.20000 55.100 5 5 35.125000
## 583 35.900 42.900 2.10000 10.600 2 2 58.285714
## 589 14.100 6.900 14.10000 39.000 3 3 57.500000
## 603 3.700 4.300 16.00000 57.700 6 6 52.666667
## 604 20.600 9.500 20.50000 43.800 1 1 45.750000
## 610 12.700 7.100 23.30000 49.700 4 4 48.833333
## 611 12.700 7.100 23.30000 49.700 4 4 46.363636
## 614 12.300 8.900 10.70000 44.700 6 6 19.500000
## 616 31.700 20.300 12.10000 24.900 1 3 56.181818
## 617 25.000 20.100 11.60000 26.000 5 5 16.583333
## 619 13.600 4.600 9.50000 53.500 5 5 35.200000
## 621 26.200 13.000 7.90000 27.900 4 4 27.285714
## 622 18.300 3.500 7.10000 41.400 6 6 23.375000
## 625 16.200 7.900 6.90000 44.600 2 2 51.636364
## 626 7.100 3.300 22.40000 57.300 4 4 36.818182
## 628 17.000 13.300 35.70000 46.600 1 1 80.000000
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## id.y .pred .row value.y .config
## 3 train/test split 11.951434 3 11.212174 Preprocessor1_Model1
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## 33 train/test split 8.579986 33 5.528205 Preprocessor1_Model1
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## 723 train/test split 11.613684 723 11.020625 Preprocessor1_Model1
## 725 train/test split 13.101147 725 13.402367 Preprocessor1_Model1
## 728 train/test split 9.736534 728 10.678632 Preprocessor1_Model1
## 730 train/test split 9.575450 730 9.027193 Preprocessor1_Model1
## 734 train/test split 10.947530 734 12.163063 Preprocessor1_Model1
## 735 train/test split 11.401383 735 11.410909 Preprocessor1_Model1
## 741 train/test split 11.734060 741 12.383193 Preprocessor1_Model1
## 749 train/test split 7.389263 749 5.252941 Preprocessor1_Model1
## 752 train/test split 8.155078 752 7.740496 Preprocessor1_Model1
## 753 train/test split 8.238650 753 6.715126 Preprocessor1_Model1
## 756 train/test split 11.869730 756 12.578903 Preprocessor1_Model1
## 759 train/test split 10.869823 759 11.167213 Preprocessor1_Model1
## 762 train/test split 11.189223 762 11.487719 Preprocessor1_Model1
## 765 train/test split 10.243987 765 9.272131 Preprocessor1_Model1
## 768 train/test split 10.591787 768 11.859016 Preprocessor1_Model1
## 769 train/test split 10.521802 769 16.151786 Preprocessor1_Model1
## 770 train/test split 11.724622 770 11.776271 Preprocessor1_Model1
## 771 train/test split 11.715460 771 10.778947 Preprocessor1_Model1
## 785 train/test split 9.149385 785 9.704315 Preprocessor1_Model1
## 797 train/test split 9.337306 797 8.654310 Preprocessor1_Model1
## 800 train/test split 9.126558 800 7.759574 Preprocessor1_Model1
## 805 train/test split 11.751686 805 11.301639 Preprocessor1_Model1
## 807 train/test split 11.472187 807 11.229167 Preprocessor1_Model1
## 809 train/test split 11.020510 809 12.063333 Preprocessor1_Model1
## 814 train/test split 10.904728 814 11.498198 Preprocessor1_Model1
## 815 train/test split 11.024425 815 10.589167 Preprocessor1_Model1
## 818 train/test split 11.891652 818 13.664641 Preprocessor1_Model1
## 820 train/test split 11.199264 820 11.701420 Preprocessor1_Model1
## 830 train/test split 12.342700 830 14.158261 Preprocessor1_Model1
## 837 train/test split 12.613581 837 14.201709 Preprocessor1_Model1
## 838 train/test split 12.173873 838 13.716379 Preprocessor1_Model1
## 843 train/test split 12.966254 843 13.856637 Preprocessor1_Model1
## 845 train/test split 11.071637 845 11.751882 Preprocessor1_Model1
## 851 train/test split 10.579716 851 11.835537 Preprocessor1_Model1
## 855 train/test split 11.835620 855 12.694958 Preprocessor1_Model1
## 856 train/test split 11.950616 856 13.318333 Preprocessor1_Model1
## 857 train/test split 11.840416 857 13.058333 Preprocessor1_Model1
## 859 train/test split 10.755876 859 11.272500 Preprocessor1_Model1
## 864 train/test split 11.272888 864 13.546400 Preprocessor1_Model1
## 870 train/test split 8.258503 870 4.500000 Preprocessor1_Model1
## 874 train/test split 6.589595 874 6.173494 Preprocessor1_Model1Results
The Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) using the roc and auc functions from the pROC package. It assesses the predictive performance of a binary classification model using the actual values in value.y and the predicted probabilities or scores in .pred. The AUC provides a single metric representing the model’s ability to discriminate between the two classes, with a higher AUC indicating better performance.
roc_auc <- roc(cleaned_data$value.y, cleaned_data$.pred)## Warning in roc.default(cleaned_data$value.y, cleaned_data$.pred): 'response'
## has more than two levels. Consider setting 'levels' explicitly or using
## 'multiclass.roc' instead## Setting levels: control = 4.298275862, case = 4.318333333## Setting direction: controls > casesauc(roc_auc)## Area under the curve: 1The function yardstick::metrics() is utilized to compute performance evaluation metrics within the dataset cleaned_data. It is intended to measure the predictive accuracy of a model by comparing the predicted values (.pred) against the actual or ground truth values (value.y). The specific metrics being calulated are rsme, mae, and rsq.
yardstick::metrics(cleaned_data,
truth = value.y, estimate = .pred)## # A tibble: 3 × 3
## .metric .estimator .estimate
## <chr> <chr> <dbl>
## 1 rmse standard 1.72
## 2 rsq standard 0.610
## 3 mae standard 1.16# fit data
RF_final_train_fit <- parsnip::fit(RF_tuned_wflow, data = train_pm)
RF_final_test_fit <- parsnip::fit(RF_tuned_wflow, data = test_pm)
# get predictions on training data
values_pred_train <- predict(RF_final_train_fit, train_pm) %>%
bind_cols(train_pm %>% select(value, fips, county, id, zcta, lon, lat))
# get predictions on testing data
values_pred_test <- predict(RF_final_test_fit, test_pm) %>%
bind_cols(test_pm %>% select(value, fips, county, id, zcta, lon, lat))
# combine
all_pred <- bind_rows(values_pred_test, values_pred_train)
all_pred## # A tibble: 876 × 8
## .pred value fips county id zcta lon lat
## <dbl> <dbl> <fct> <chr> <fct> <fct> <dbl> <dbl>
## 1 11.5 11.2 1033 Colbert 1033.1002 35660 -87.7 34.8
## 2 11.9 12.4 1055 Etowah 1055.001 35901 -86.0 34.0
## 3 11.1 10.5 1069 Houston 1069.0003 36303 -85.4 31.2
## 4 13.8 15.6 1073 Jefferson 1073.0023 35207 -86.8 33.6
## 5 12.1 12.4 1073 Jefferson 1073.1005 35111 -87.0 33.3
## 6 11.3 11.1 1073 Jefferson 1073.1009 35444 -87.3 33.5
## 7 11.6 11.8 1073 Jefferson 1073.5003 35180 -86.9 33.8
## 8 11.0 10.0 1097 Mobile 1097.0003 36611 -88.1 30.8
## 9 11.9 12.0 1101 Montgomery 1101.0007 36110 -86.3 32.4
## 10 12.7 13.2 1113 Russell 1113.0001 31901 -85.0 32.5
## # ℹ 866 more rowsall_pred %>% group_by(zcta) %>%
summarize(mean_predictions = mean(.pred), mean_actual = mean(value))## # A tibble: 842 × 3
## zcta mean_predictions mean_actual
## <fct> <dbl> <dbl>
## 1 1022 9.39 9.27
## 2 1103 10.6 10.8
## 3 1201 9.54 9.96
## 4 1608 10.0 10.0
## 5 1832 8.95 8.55
## 6 1840 9.29 9.05
## 7 1863 9.15 8.70
## 8 1904 9.47 8.71
## 9 2113 10.5 11.1
## 10 2119 10.2 9.82
## # ℹ 832 more rowspred <- ggplot(all_pred, aes(x = lon, y = lat, color = .pred)) +
geom_jitter() +
labs(x = "Longitude", y = "Latitude", color = "Predictions") +
scale_color_continuous(limits = c(0, 25), low = "#deebf7", high = "#3182bd")
truth <- ggplot(all_pred, aes(x = lon, y = lat, color = value)) +
geom_jitter() +
labs(x = "Longitude", y = "Latitude", color = "True Value") +
scale_color_continuous(limits = c(0, 25), low = "#deebf7", high = "#3182bd")
(pred/truth) + plot_annotation(title = "Machine Learning Methods Allow for Prediction of Air Pollution", subtitle = "A random forest model predicts true monitored levels of fine particulate matter (PM 2.5) air pollution based on\ndata about population density and other predictors reasonably well, thus suggesting that we can use similar methods to predict levels\nof pollution in places with poor monitoring",
theme = theme(plot.title = element_text(size =12, face = "bold"),
plot.subtitle = element_text(size = 8)))
Conclusion
We used ChatGPT to help us phrase the conclusion.
The study aimed to forecast annual average air pollution levels across US zip code regions, leveraging diverse predictors encompassing population density, urbanization metrics, road density data, alongside satellite pollution and chemical modeling information. The predictive model exhibited promising outcomes, demonstrating high accuracy with a ROC AUC score of 1, indicating impeccable discrimination between the predicted and actual values. The study’s predictive model showcased robust performance, reflected in multiple evaluation metrics. The Root Mean Square Error (RMSE) stood at 1.716, indicating the average deviation of predicted pollution concentrations from actual values, suggesting a relatively low average prediction error. Additionally, the R-squared (RSQ) value of 0.61 denotes a substantial proportion of variance in the pollution levels explained by the model, signifying its capability to capture and account for variability. Furthermore, the Mean Absolute Error (MAE) of 1.159 implies the average absolute deviation between predicted and observed values, further substantiating the model’s accuracy in approximating actual pollution levels at the zip code level. These metrics collectively demonstrate the model’s strong predictive ability and its capacity to effectively leverage the diverse set of predictors for forecasting air pollution concentrations. Due to some installation issues with packages such as rnaturalearth and sf, we decided to plot our predictions and true values using longitude and latitude. By plotting the longitude and latitude, we were able to visualize every zip code in our dataset and compare their predicted with their true value. Overall, our model does a fairly good job predicting pollution in zip codes that aren’t heavily populated. We noticed in higher populated areas such as Los Angeles and New York City, the pollution true values are very high, that our model predicts their pollution rate to be lower than they truly are. Assessing feature importance revealed intriguing insights, notably highlighting ‘state’ as the most influential predictor, followed by ‘CMAQ,’ both substantially more impactful than the other top-ranking variables in the model. This suggests that regional differentiation, potentially tied to state-specific policies or geographically bound factors, heavily influences air pollution levels, emphasizing the significance of localized factors in these predictions.
Extension
For our extension, we chose to explore an additional factor that we thought could impact air pollution: airports. The National Library of Medicine states that “In general, most on-airport studies in the U. S. showed slightly elevated concentrations of gaseous criteria pollutants, specifically carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2), even though concentrations are often still below national ambient air quality standards.” [2] Though the concentrations are still below the national standards, this case study is about predicting future pollutant causes and it has been determined that airports show an elevated increase in air pollution in their respective areas. For our extension we will look deeper at these air pollution levels by county to see just how elevated they are in comparison to non-airport counties.
According to our research, the [1] Federal Aviation Administration has determined categories for all commerical airports deeming them one of the following: large hub, medium hub, small hub, or non-hub.
Large Hub: Receives 1 percent or more of the annual U.S. commercial enplanements
Medium Hub: Receives 0.25 to 1.0 percent of the annual U.S. commercial enplanements
Small Hub: Receives 0.05 to 0.25 percent of the annual U.S. commercial enplanements
Nonhub: Receives less than 0.05 percent but more than 10,000 of the annual U.S. commercial enplanement
With these categories in mind, we found it best to just look at the Medium and Large hubs as they would only make up around 1 percent of the U.S’s flights per year. We created our own dataset for this as well and it is incredibly difficult to verify that we accounted for all non-hubs and small-hubs such as privately owned airports and ones not listed. To clarify, the dataset that we created called airports2.csv contains only airports with labels “P-L” and “P-M” meaning Large hub and medium hub respectively.
[1] “Airport Categories.” Airport Categories | Federal Aviation Administration, www.faa.gov/airports/planning_capacity/categories. Accessed 12 Dec. 2023.
[2] Riley, Karie, et al. “A Systematic Review of The Impact of Commercial Aircraft Activity on Air Quality Near Airports.” City and Environment Interactions, U.S. National Library of Medicine, www.ncbi.nlm.nih.gov/pmc/articles/PMC8318113/#:~:text=In%20general%2C%20most%20on%2Dairport,national%20ambient%20air%20quality%20standards. Accessed 12 Dec. 2023.
pm <- pm %>% mutate(has_airport = ifelse(county %in% airports$County, 'Y', 'N'))
hasAP <- ggplot(pm, aes(x = has_airport, y = value)) +
geom_boxplot() +
labs(title = "Air Pollution Levels by Airport",
x = "Has Airport",
y = "Air Pollution Level") +
theme_minimal()
hasAP
The chart above shows us that the median pollution level for a county that has an airport is higher than that of a county that doesn’t have an airport. To see exactly how much data we can attribute to having an airport in a county or not, we can make a linear model to give us an R-squared value aka a coefficient of determination.
linear_model <- lm(value ~ has_airport, data = pm)
summary(linear_model)##
## Call:
## lm(formula = value ~ has_airport, data = pm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.731 -1.514 0.350 1.571 12.406
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10.75440 0.09739 110.431 <2e-16 ***
## has_airportY 0.27044 0.21914 1.234 0.217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.582 on 874 degrees of freedom
## Multiple R-squared: 0.00174, Adjusted R-squared: 0.0005974
## F-statistic: 1.523 on 1 and 874 DF, p-value: 0.2175Using a linear model, we can determine just how impactful the presence of an airport has on the air pollution levels for the counties listed in our data set. According to the linera model, less than one percent of the variability in the air pollution levels can be explained by whether there is an airport located in a county or not. This is a small R squared value and tells us that not much of the data can be explained but any non-zero value is interesting. Again there are probably other factors as we determined earlier in the case study that had more of an impact on air pollution but it is interesting to see that there is a slight increase in air pollution.
Let’s now take a look at specifically the top five percent of air pollution contributors in the United States to see if the higher producing polluters also have airports in them.
ChatGPT helped us make this graph especially with collecting the top 5 percent
percentile_95 <- quantile(pm$value, 0.95)
pm <- pm %>% mutate(top_5_percent = ifelse(value > percentile_95, 'Top 5%', 'Bottom 95%'))
top_5_pm <- pm %>% filter(top_5_percent == 'Top 5%')
CountyAP <- ggplot(top_5_pm, aes(x = reorder(county, -value), y = value, fill = has_airport)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Top 5% of Air Pollution Levels by County and Airport Presence",
x = "County",
y = "Air Pollution Level",
fill = "Has Airport") +
theme_minimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
CountyAP
As we can see here, 9 of the 30 counties had airports. This doesn’t look like much but the math shows us that approximately 30% of the top producing air polluting counties also contain airports in them. 30% is a decent percentage but when further examined the counties associated with each airport are also major metropolitan areas and we determined earlier that areas highly populated areas have a higher pollution levels.
We mentioned earlier that the FAA actually separates each airport by levels determined by the amount of flights out of the total flight count in the united states with the largest contributors to total flights as large hubs and second largest as medium hubs. Even though we determined that airports aren’t a great predictor in the air pollution levels of a specific county, we wanted to see if there was a noticeable difference between the large and medium hubs.
ChatGPT helped us merge data
AVC <- merge(pm, airports, by.x = "county", by.y = "County", all.x = TRUE)
ggplot(AVC, aes(x = has_airport, y = value, fill = FAARating)) +
geom_boxplot() + labs(title = "Boxplot of Air Pollution Levels by FAA Rating and Airport Presence",
x = "Has Airport",
y = "Air Pollution Level",
fill = "FAA Rating") +
theme_minimal()
Here we can see that there is a slightly noticeable difference between
medium hubs and large hubs but the interesting part is that the medium
hubs are higher than the large ones. This tells us that it is even more
likely for the size of airports to contribute to air pollution as
previously thought because the airports with less flights actually have
higher pollution rates. Due to the incredibly minimal gab between the
medium and large levels, it seems pretty safe to assume that there are
other factors such as state and CMAQ that would be better predictors in
the future.
Based on all of the additional data that we have collected, it is reasonable to conclude that airports are not a huge indicator of levels of air pollution but a better indicator of a highly populated county. We found previously that our model does not account for highly populated areas as accurately as we would like but potentially as we saw earlier further exploration with state as our most accurate predictor, potentially combined with airports of the large hub level could add a slightly higher level of certainty. Though our results didn’t indicate a perfect prediction of air pollution levels, it is important to note that in both cases the coefficient of determination was not zero this means that some, though very little, of the data can be explained by air traffic but anything above zero needs to be considered in context. As of right now we think it is safe to continue with air travel and not have to fret too much about fumes from the plane but having a non-zero coefficient of determination tells us there could be some contributions to air pollution from these airports and we as a group believe this could be used as evidence for further research of alternative fuels.